diff --git a/404.html b/404.html
index 02e0377..b6c70c9 100644
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@@ -8,62 +8,51 @@
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+
     <a class="navbar-brand me-2" href="https://computational-metabolomics.github.io/structToolbox/index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.17.3</small>
+
 
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+<li class="nav-item"><a class="nav-link" href="https://computational-metabolomics.github.io/structToolbox/reference/index.html">Reference</a></li>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles">
+<li><a class="dropdown-item" href="https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
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-<form class="form-inline my-2 my-lg-0" role="search">
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+<ul class="navbar-nav">
+<li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="search.json">
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@@ -73,21 +62,19 @@
 
 
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+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
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+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
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diff --git a/LICENSE-text.html b/LICENSE-text.html
index e03cf31..2ea899f 100644
--- a/LICENSE-text.html
+++ b/LICENSE-text.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
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-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary"><div class="container">
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+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.17.3</small>
+
 
-    
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+      <ul class="navbar-nav me-auto"><li class="nav-item"><a class="nav-link" href="reference/index.html">Reference</a></li>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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 </nav><div class="container template-title-body">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>License</h1>
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@@ -727,18 +715,18 @@
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
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-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
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diff --git a/articles/data_analysis_omics_using_the_structtoolbox.html b/articles/data_analysis_omics_using_the_structtoolbox.html
index 08d0061..3d938bc 100644
--- a/articles/data_analysis_omics_using_the_structtoolbox.html
+++ b/articles/data_analysis_omics_using_the_structtoolbox.html
@@ -5,61 +5,47 @@
 <meta charset="utf-8">
 <meta http-equiv="X-UA-Compatible" content="IE=edge">
 <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
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 <title>Data analysis of metabolomics and other omics datasets using the structToolbox • structToolbox</title>
 <script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
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@@ -68,7 +54,8 @@
 
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Data analysis of metabolomics and other omics datasets using the structToolbox</h1>
+
+      <h1>Data analysis of metabolomics and other omics datasets using the structToolbox</h1>
                         <h4 data-toc-skip class="author">Gavin R
 Lloyd</h4>
             <address class="author_afil">
@@ -89,7 +76,7 @@ <h4 data-toc-skip class="author">Ralf J
       </address>
                   
       
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/vignettes/data_analysis_omics_using_the_structtoolbox.Rmd" class="external-link"><code>vignettes/data_analysis_omics_using_the_structtoolbox.Rmd</code></a></small>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/vignettes/data_analysis_omics_using_the_structtoolbox.Rmd" class="external-link"><code>vignettes/data_analysis_omics_using_the_structtoolbox.Rmd</code></a></small>
       <div class="d-none name"><code>data_analysis_omics_using_the_structtoolbox.Rmd</code></div>
     </div>
 
@@ -1236,19 +1223,19 @@ <h3 id="univariate-statistics">Univariate statistics<a class="anchor" aria-label
 <span><span class="co"># show first few features</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">out</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">##     t_statistic   t_p_value t_significant estimate.mean.GC estimate.mean.HE</span></span>
-<span><span class="co">## M4   -3.5392652 0.008421042          TRUE         26.47778         51.73947</span></span>
-<span><span class="co">## M5    1.4296604 0.410396437         FALSE        265.11860        169.91500</span></span>
-<span><span class="co">## M7    2.7456506 0.051494976         FALSE        118.52558         53.98718</span></span>
-<span><span class="co">## M8   -2.1294198 0.178392032         FALSE         54.39535         79.26750</span></span>
-<span><span class="co">## M11   0.5106536 0.776939682         FALSE        201.34390        171.27949</span></span>
-<span><span class="co">## M14  -1.4786810 0.403091881         FALSE         61.53171         83.90250</span></span>
-<span><span class="co">##         lower      upper</span></span>
-<span><span class="co">## M4  -39.56162 -10.961769</span></span>
-<span><span class="co">## M5  -38.04747 228.454679</span></span>
-<span><span class="co">## M7   17.60818 111.468619</span></span>
-<span><span class="co">## M8  -48.20069  -1.543611</span></span>
-<span><span class="co">## M11 -87.30604 147.434869</span></span>
-<span><span class="co">## M14 -52.57754   7.835950</span></span></code></pre>
+<span><span class="co">## M4    3.5392652 0.008421042          TRUE         51.73947         26.47778</span></span>
+<span><span class="co">## M5   -1.4296604 0.410396437         FALSE        169.91500        265.11860</span></span>
+<span><span class="co">## M7   -2.7456506 0.051494976         FALSE         53.98718        118.52558</span></span>
+<span><span class="co">## M8    2.1294198 0.178392032         FALSE         79.26750         54.39535</span></span>
+<span><span class="co">## M11  -0.5106536 0.776939682         FALSE        171.27949        201.34390</span></span>
+<span><span class="co">## M14   1.4786810 0.403091881         FALSE         83.90250         61.53171</span></span>
+<span><span class="co">##           lower     upper</span></span>
+<span><span class="co">## M4    10.961769  39.56162</span></span>
+<span><span class="co">## M5  -228.454679  38.04747</span></span>
+<span><span class="co">## M7  -111.468619 -17.60818</span></span>
+<span><span class="co">## M8     1.543611  48.20069</span></span>
+<span><span class="co">## M11 -147.434869  87.30604</span></span>
+<span><span class="co">## M14   -7.835950  52.57754</span></span></code></pre>
 </div>
 <div class="section level3">
 <h3 id="multivariate-statistics-and-machine-learning">Multivariate statistics and machine learning<a class="anchor" aria-label="anchor" href="#multivariate-statistics-and-machine-learning"></a>
@@ -1587,9 +1574,11 @@ <h3 id="svm-cost-function">SVM cost function<a class="anchor" aria-label="anchor
 <span>  <span class="co"># create some space to the left of the legend</span></span>
 <span>  <span class="va">g1</span> <span class="op">+</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/guides.html" class="external-link">guides</a></span><span class="op">(</span>color <span class="op">=</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/guide_legend.html" class="external-link">guide_legend</a></span><span class="op">(</span>nrow <span class="op">=</span> <span class="fl">1</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
 <span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/theme.html" class="external-link">theme</a></span><span class="op">(</span>legend.position <span class="op">=</span> <span class="st">"bottom"</span><span class="op">)</span></span>
-<span><span class="op">)</span></span>
-<span></span>
-<span><span class="fu"><a href="https://wilkelab.org/cowplot/reference/plot_grid.html" class="external-link">plot_grid</a></span><span class="op">(</span><span class="va">prow</span>, <span class="va">legend</span>, ncol<span class="op">=</span><span class="fl">1</span>, rel_heights <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">.1</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
+<span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## Warning in get_plot_component(plot, "guide-box"): Multiple components found;</span></span>
+<span><span class="co">## returning the first one. To return all, use `return_all = TRUE`.</span></span></code></pre>
+<div class="sourceCode" id="cb98"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://wilkelab.org/cowplot/reference/plot_grid.html" class="external-link">plot_grid</a></span><span class="op">(</span><span class="va">prow</span>, <span class="va">legend</span>, ncol<span class="op">=</span><span class="fl">1</span>, rel_heights <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">.1</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
 <p><img src="data_analysis_omics_using_the_structtoolbox_files/figure-html/unnamed-chunk-62-1.png"><!-- --></p>
 </div>
 <div class="section level3">
@@ -1602,7 +1591,7 @@ <h3 id="kernel-functions">Kernel functions<a class="anchor" aria-label="anchor"
 additional parameters to be specified. The best kernel to use will vary
 depending on the dataset, but a common choice is the radial kernel as it
 allows high flexibility with a single parameter.</p>
-<div class="sourceCode" id="cb97"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb99"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># set a fixed cost for this comparison</span></span>
 <span><span class="va">M</span><span class="op">$</span><span class="va">cost</span><span class="op">=</span><span class="fl">1</span></span>
 <span></span>
@@ -1650,15 +1639,18 @@ <h3 id="kernel-functions">Kernel functions<a class="anchor" aria-label="anchor"
 <span>  <span class="co"># create some space to the left of the legend</span></span>
 <span>  <span class="va">g1</span> <span class="op">+</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/guides.html" class="external-link">guides</a></span><span class="op">(</span>color <span class="op">=</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/guide_legend.html" class="external-link">guide_legend</a></span><span class="op">(</span>nrow <span class="op">=</span> <span class="fl">1</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
 <span>    <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/theme.html" class="external-link">theme</a></span><span class="op">(</span>legend.position <span class="op">=</span> <span class="st">"bottom"</span><span class="op">)</span></span>
-<span><span class="op">)</span></span>
-<span><span class="fu"><a href="https://wilkelab.org/cowplot/reference/plot_grid.html" class="external-link">plot_grid</a></span><span class="op">(</span><span class="va">prow</span>, <span class="va">legend</span>, ncol <span class="op">=</span> <span class="fl">1</span>, rel_heights <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">.1</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
+<span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## Warning in get_plot_component(plot, "guide-box"): Multiple components found;</span></span>
+<span><span class="co">## returning the first one. To return all, use `return_all = TRUE`.</span></span></code></pre>
+<div class="sourceCode" id="cb101"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://wilkelab.org/cowplot/reference/plot_grid.html" class="external-link">plot_grid</a></span><span class="op">(</span><span class="va">prow</span>, <span class="va">legend</span>, ncol <span class="op">=</span> <span class="fl">1</span>, rel_heights <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">.1</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
 <p><img src="data_analysis_omics_using_the_structtoolbox_files/figure-html/unnamed-chunk-63-1.png"><!-- --></p>
 <p>The parameters of a kernel can be used to control the complexity of
 the boundary. Here I show how the radial kernel parameter “gamma” can be
 used to change the complexity of the boundary. In combination with the
 cost parameter (which I keep constant here) this allows for highly
 flexible boundary models.</p>
-<div class="sourceCode" id="cb98"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb102"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># rbf kernel and cost</span></span>
 <span><span class="va">M</span><span class="op">$</span><span class="va">kernel</span> <span class="op">=</span> <span class="st">'radial'</span></span>
 <span><span class="va">M</span><span class="op">$</span><span class="va">cost</span> <span class="op">=</span> <span class="fl">1</span></span>
@@ -1695,8 +1687,11 @@ <h3 id="kernel-functions">Kernel functions<a class="anchor" aria-label="anchor"
 <span>  <span class="co"># create some space to the left of the legend</span></span>
 <span>  <span class="va">g1</span> <span class="op">+</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/guides.html" class="external-link">guides</a></span><span class="op">(</span>color <span class="op">=</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/guide_legend.html" class="external-link">guide_legend</a></span><span class="op">(</span>nrow <span class="op">=</span> <span class="fl">1</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
 <span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/theme.html" class="external-link">theme</a></span><span class="op">(</span>legend.position <span class="op">=</span> <span class="st">"bottom"</span><span class="op">)</span></span>
-<span><span class="op">)</span></span>
-<span><span class="fu"><a href="https://wilkelab.org/cowplot/reference/plot_grid.html" class="external-link">plot_grid</a></span><span class="op">(</span><span class="va">prow</span>, <span class="va">legend</span>, ncol <span class="op">=</span> <span class="fl">1</span>, rel_heights <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">.1</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
+<span><span class="op">)</span></span></code></pre></div>
+<pre><code><span><span class="co">## Warning in get_plot_component(plot, "guide-box"): Multiple components found;</span></span>
+<span><span class="co">## returning the first one. To return all, use `return_all = TRUE`.</span></span></code></pre>
+<div class="sourceCode" id="cb104"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://wilkelab.org/cowplot/reference/plot_grid.html" class="external-link">plot_grid</a></span><span class="op">(</span><span class="va">prow</span>, <span class="va">legend</span>, ncol <span class="op">=</span> <span class="fl">1</span>, rel_heights <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">.1</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
 <p><img src="data_analysis_omics_using_the_structtoolbox_files/figure-html/unnamed-chunk-64-1.png"><!-- --></p>
 <p>Note that best practice would be to select the optimal kernel
 parameter(s) in combination with the cost parameter (e.g. by 2d grid
@@ -1734,7 +1729,7 @@ <h3 id="lc-ms-based-proteomics-dataset">LC-MS-based proteomics dataset<a class="
 <p>The LC-MS-based proteomics dataset from the STATegra multi-omics
 dataset (see Introduction) can be found on <a href="https://github.com/STATegraData/STATegraData" class="external-link">github</a> and must
 be extracted from the zip file prior to data analysis.</p>
-<div class="sourceCode" id="cb99"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb105"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># path to zip</span></span>
 <span><span class="va">zipfile</span> <span class="op">=</span> <span class="st">"https://raw.github.com/STATegraData/STATegraData/master/Script_STATegra_Proteomics.zip"</span></span>
 <span></span>
@@ -1754,7 +1749,7 @@ <h3 id="lc-ms-based-proteomics-dataset">LC-MS-based proteomics dataset<a class="
 <p>The imported data needs to be converted to
 <code>DatasetExperiment</code> format for use with
 <code>structToolbox</code>.</p>
-<div class="sourceCode" id="cb100"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb106"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># extract data matrix</span></span>
 <span><span class="va">data</span> <span class="op">=</span> <span class="va">all_data</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">2527</span>,<span class="fl">51</span><span class="op">:</span><span class="fl">86</span><span class="op">]</span></span>
 <span><span class="co"># shorten sample names</span></span>
@@ -1805,7 +1800,7 @@ <h3 id="lc-ms-based-proteomics-dataset">LC-MS-based proteomics dataset<a class="
 <span><span class="co">## variable_meta: 2527 rows x 3 columns</span></span></code></pre>
 <p>A number of Reporter genes were included in the study. We plot two of
 them here to illustrate some trends in the data.</p>
-<div class="sourceCode" id="cb102"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb108"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># find id of reporters</span></span>
 <span><span class="va">Ldha</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/which.html" class="external-link">which</a></span><span class="op">(</span><span class="va">DS</span><span class="op">$</span><span class="va">variable_meta</span><span class="op">$</span><span class="va">Gene.names</span><span class="op">==</span><span class="st">'Ldha'</span><span class="op">)</span></span>
 <span><span class="va">Hk2</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/which.html" class="external-link">which</a></span><span class="op">(</span><span class="va">DS</span><span class="op">$</span><span class="va">variable_meta</span><span class="op">$</span><span class="va">Gene.names</span><span class="op">==</span><span class="st">'Hk2'</span><span class="op">)</span></span>
@@ -1825,7 +1820,7 @@ <h4 id="data-transformation">Data transformation<a class="anchor" aria-label="an
 medians is equal for all conditions. These steps are available in
 <code>structToolbox</code> using <code>log_transform</code> and
 <code>mean_of_medians</code> objects.</p>
-<div class="sourceCode" id="cb103"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb109"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># prepare model sequence</span></span>
 <span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="../reference/log_transform.html">log_transform</a></span><span class="op">(</span></span>
 <span>  base<span class="op">=</span><span class="fl">2</span><span class="op">)</span> <span class="op">+</span> </span>
@@ -1837,7 +1832,7 @@ <h4 id="data-transformation">Data transformation<a class="anchor" aria-label="an
 <span><span class="co"># get transformed data</span></span>
 <span><span class="va">DST</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/struct/man/predicted.html" class="external-link">predicted</a></span><span class="op">(</span><span class="va">M</span><span class="op">)</span></span></code></pre></div>
 <p>The Reporter genes are plotted again for comparison.</p>
-<div class="sourceCode" id="cb104"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb110"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># chart object</span></span>
 <span><span class="va">C</span> <span class="op">=</span> <span class="fu"><a href="../reference/feature_boxplot.html">feature_boxplot</a></span><span class="op">(</span>feature_to_plot<span class="op">=</span><span class="va">Ldha</span>,factor_name<span class="op">=</span><span class="st">'time'</span>,label_outliers<span class="op">=</span><span class="cn">FALSE</span><span class="op">)</span></span>
 <span><span class="va">g1</span><span class="op">=</span><span class="fu"><a href="../reference/chart_plot.html">chart_plot</a></span><span class="op">(</span><span class="va">C</span>,<span class="va">DST</span><span class="op">)</span><span class="op">+</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/labs.html" class="external-link">ggtitle</a></span><span class="op">(</span><span class="st">'Ldha'</span><span class="op">)</span><span class="op">+</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/labs.html" class="external-link">ylab</a></span><span class="op">(</span><span class="st">'log2(expression)'</span><span class="op">)</span></span>
@@ -1867,7 +1862,7 @@ <h4 id="missing-value-filtering">Missing value filtering<a class="anchor" aria-l
 changed, so the <code>filter_na_count</code> and
 <code>filter_by_name</code> objects are working together as a single
 filter.</p>
-<div class="sourceCode" id="cb105"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb111"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># build model sequence</span></span>
 <span><span class="va">M2</span> <span class="op">=</span> <span class="fu"><a href="../reference/filter_na_count.html">filter_na_count</a></span><span class="op">(</span></span>
 <span>  threshold<span class="op">=</span><span class="fl">2</span>,</span>
@@ -1902,7 +1897,7 @@ <h4 id="missing-value-imputation">Missing value imputation<a class="anchor" aria
 discovery”. We create a new struct object using
 <code>set_struct_obj</code> in the global environment, and a
 “method_apply” method that implements the imputation.</p>
-<div class="sourceCode" id="cb106"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb112"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># create new imputation object</span></span>
 <span><span class="fu"><a href="https://rdrr.io/pkg/struct/man/set_struct_obj.html" class="external-link">set_struct_obj</a></span><span class="op">(</span></span>
 <span>  class_name <span class="op">=</span> <span class="st">'STATegra_impute1'</span>,</span>
@@ -1954,7 +1949,7 @@ <h4 id="missing-value-imputation">Missing value imputation<a class="anchor" aria
 condition with exactly 1 missing value with the mean of the values for
 that condition. Again we create a new struct object and corresponding
 method for the the new object to implement the filter.</p>
-<div class="sourceCode" id="cb107"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb113"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># create new imputation object</span></span>
 <span><span class="fu"><a href="https://rdrr.io/pkg/struct/man/set_struct_obj.html" class="external-link">set_struct_obj</a></span><span class="op">(</span></span>
 <span>  class_name <span class="op">=</span> <span class="st">'STATegra_impute2'</span>,</span>
@@ -2020,7 +2015,7 @@ <h4 id="missing-value-imputation">Missing value imputation<a class="anchor" aria
 sequences like any other <code>struct</code> object. A final filter is
 added to remove any feature that has missing values after
 imputation.</p>
-<div class="sourceCode" id="cb108"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb114"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># model sequence</span></span>
 <span><span class="va">M3</span> <span class="op">=</span> <span class="fu">STATegra_impute1</span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'treatment'</span>,factor_sd<span class="op">=</span><span class="st">'condition'</span><span class="op">)</span> <span class="op">+</span></span>
 <span>     <span class="fu">STATegra_impute2</span><span class="op">(</span>factor_name <span class="op">=</span> <span class="st">'condition'</span><span class="op">)</span> <span class="op">+</span> </span>
@@ -2052,7 +2047,7 @@ <h4 id="exploratory-analysis-1">Exploratory analysis<a class="anchor" aria-label
 <p>PCA is used to provide a graphical representation of the data. For
 comparison with the outputs from STATegra a filter is included to reduce
 the data to include only the treated samples (IKA)</p>
-<div class="sourceCode" id="cb110"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb116"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># model sequence</span></span>
 <span><span class="va">P</span> <span class="op">=</span> <span class="fu"><a href="../reference/filter_smeta.html">filter_smeta</a></span><span class="op">(</span>mode<span class="op">=</span><span class="st">'include'</span>,factor_name<span class="op">=</span><span class="st">'treatment'</span>,levels<span class="op">=</span><span class="st">'IKA'</span><span class="op">)</span> <span class="op">+</span></span>
 <span>    <span class="fu"><a href="../reference/mean_centre.html">mean_centre</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span> </span>
@@ -2080,7 +2075,7 @@ <h3 id="lc-ms-based-metabolomics-dataset">LC-MS-based metabolomics dataset<a cla
 <p>The LC-MS-based metabolomics dataset from the STATegra multi-omics
 dataset (see Introduction) can be found on <a href="https://github.com/STATegraData/STATegraData" class="external-link">github</a> and must
 be extracted from zip file prior to data analysis.</p>
-<div class="sourceCode" id="cb111"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb117"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># path to zip</span></span>
 <span><span class="va">zipfile</span> <span class="op">=</span> <span class="st">"https://raw.github.com/STATegraData/STATegraData/master/Script_STATegra_Metabolomics.zip"</span></span>
 <span></span>
@@ -2101,7 +2096,7 @@ <h3 id="lc-ms-based-metabolomics-dataset">LC-MS-based metabolomics dataset<a cla
 <p>The imported data needs to be converted to
 <code>DatasetExperiment</code> format for use with
 <code>structToolbox</code>.</p>
-<div class="sourceCode" id="cb112"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb118"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># extract sample meta data</span></span>
 <span><span class="va">SM</span> <span class="op">=</span> <span class="va">data</span><span class="op">[</span> ,<span class="fl">1</span><span class="op">:</span><span class="fl">8</span><span class="op">]</span></span>
 <span></span>
@@ -2164,7 +2159,7 @@ <h4 id="data-preprocessing-1">Data preprocessing<a class="anchor" aria-label="an
 so the data will be processed differently in comparison to <a href="https://www.nature.com/articles/s41597-019-0202-7" class="external-link">Gomez-Cabrero
 et al</a>. Some basic processing steps will be applied in order to
 generate a valid PCA plot from the biological and QC samples.</p>
-<div class="sourceCode" id="cb114"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb120"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># prepare model sequence</span></span>
 <span><span class="va">MS</span> <span class="op">=</span> <span class="fu"><a href="../reference/filter_smeta.html">filter_smeta</a></span><span class="op">(</span>mode <span class="op">=</span> <span class="st">'include'</span>, levels<span class="op">=</span><span class="st">'QC'</span>, factor_name <span class="op">=</span> <span class="st">'sample_type'</span><span class="op">)</span> <span class="op">+</span></span>
 <span>     <span class="fu"><a href="../reference/knn_impute.html">knn_impute</a></span><span class="op">(</span>neighbours<span class="op">=</span><span class="fl">5</span><span class="op">)</span> <span class="op">+</span></span>
@@ -2181,7 +2176,7 @@ <h4 id="exploratory-analysis-2">Exploratory analysis<a class="anchor" aria-label
 </h4>
 <p>First we will use PCA to look at the QC samples in order to make an
 assessment of the data quality.</p>
-<div class="sourceCode" id="cb116"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb122"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># pca model sequence</span></span>
 <span><span class="va">M</span> <span class="op">=</span>  <span class="fu"><a href="../reference/mean_centre.html">mean_centre</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
 <span>     <span class="fu"><a href="../reference/PCA.html">PCA</a></span><span class="op">(</span>number_components <span class="op">=</span> <span class="fl">3</span><span class="op">)</span></span>
@@ -2199,7 +2194,7 @@ <h4 id="exploratory-analysis-2">Exploratory analysis<a class="anchor" aria-label
 STATegra this QC was removed, so we will exclude it here as well. This
 corresponds to QC H1. STATegra also excluded QC samples measured
 immediately after a blank, which we will also do here.</p>
-<div class="sourceCode" id="cb117"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb123"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># prepare model sequence</span></span>
 <span><span class="va">MS</span> <span class="op">=</span> <span class="fu"><a href="../reference/filter_smeta.html">filter_smeta</a></span><span class="op">(</span></span>
 <span>      mode <span class="op">=</span> <span class="st">'include'</span>, </span>
@@ -2228,7 +2223,7 @@ <h4 id="exploratory-analysis-2">Exploratory analysis<a class="anchor" aria-label
 <span><span class="va">MS</span> <span class="op">=</span> <span class="fu"><a href="../reference/model_apply.html">model_apply</a></span><span class="op">(</span><span class="va">MS</span>, <span class="va">DE</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Warning in knnimp(x, k, maxmiss = rowmax, maxp = maxp): 4 rows with more than 50 % entries missing;</span></span>
 <span><span class="co">##  mean imputation used for these rows</span></span></code></pre>
-<div class="sourceCode" id="cb119"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb125"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># PCA scores plot</span></span>
 <span><span class="va">C</span> <span class="op">=</span> <span class="fu"><a href="../reference/pca_scores_plot.html">pca_scores_plot</a></span><span class="op">(</span>factor_name <span class="op">=</span> <span class="st">'sample_type'</span>,label_factor <span class="op">=</span> <span class="st">'order'</span>,points_to_label <span class="op">=</span> <span class="st">'all'</span><span class="op">)</span></span>
 <span><span class="co"># plot</span></span>
@@ -2240,7 +2235,7 @@ <h4 id="exploratory-analysis-2">Exploratory analysis<a class="anchor" aria-label
 blanks as it is likely that they will dominate the plot if not removed.
 All samples from batch 12 were excluded from STATegra and we will
 replicate that here.</p>
-<div class="sourceCode" id="cb120"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb126"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># prepare model sequence</span></span>
 <span><span class="va">MS</span> <span class="op">=</span> <span class="fu"><a href="../reference/filter_smeta.html">filter_smeta</a></span><span class="op">(</span></span>
 <span>      mode <span class="op">=</span> <span class="st">'exclude'</span>, </span>
@@ -2276,7 +2271,7 @@ <h4 id="exploratory-analysis-2">Exploratory analysis<a class="anchor" aria-label
 <span><span class="va">MS</span> <span class="op">=</span> <span class="fu"><a href="../reference/model_apply.html">model_apply</a></span><span class="op">(</span><span class="va">MS</span>, <span class="va">DE</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Warning in knnimp(x, k, maxmiss = rowmax, maxp = maxp): 2 rows with more than 50 % entries missing;</span></span>
 <span><span class="co">##  mean imputation used for these rows</span></span></code></pre>
-<div class="sourceCode" id="cb122"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb128"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># PCA scores plots</span></span>
 <span><span class="va">C</span> <span class="op">=</span> <span class="fu"><a href="../reference/pca_scores_plot.html">pca_scores_plot</a></span><span class="op">(</span>factor_name <span class="op">=</span> <span class="st">'sample_type'</span><span class="op">)</span></span>
 <span><span class="co"># plot</span></span>
@@ -2287,7 +2282,7 @@ <h4 id="exploratory-analysis-2">Exploratory analysis<a class="anchor" aria-label
 variation. There is likely to be a number of ‘low quality’ features that
 should be excluded, so we will do that now, and use more sophisticated
 normalisation (PQN) and scaling methods (glog).</p>
-<div class="sourceCode" id="cb123"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb129"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">MS</span> <span class="op">=</span>  <span class="fu"><a href="../reference/filter_smeta.html">filter_smeta</a></span><span class="op">(</span></span>
 <span>       mode <span class="op">=</span> <span class="st">'exclude'</span>, </span>
 <span>       levels <span class="op">=</span> <span class="st">'12'</span>, </span>
@@ -2378,7 +2373,7 @@ <h4 id="exploratory-analysis-2">Exploratory analysis<a class="anchor" aria-label
 condition factor variation will potentially make it easier to spot any
 more subtle trends. We will extract the glog transformed matrix from the
 previous model sequence and continue from there.</p>
-<div class="sourceCode" id="cb124"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb130"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># get the glog scaled data</span></span>
 <span><span class="va">GL</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/struct/man/predicted.html" class="external-link">predicted</a></span><span class="op">(</span><span class="va">MS</span><span class="op">[</span><span class="fl">11</span><span class="op">]</span><span class="op">)</span></span>
 <span></span>
@@ -2409,11 +2404,11 @@ <h4 id="exploratory-analysis-2">Exploratory analysis<a class="anchor" aria-label
 <div class="section level2">
 <h2 id="session-info">Session Info<a class="anchor" aria-label="anchor" href="#session-info"></a>
 </h2>
-<div class="sourceCode" id="cb125"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb131"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/sessionInfo.html" class="external-link">sessionInfo</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<pre><code><span><span class="co">## R Under development (unstable) (2024-01-23 r85822)</span></span>
+<pre><code><span><span class="co">## R version 4.4.1 (2024-06-14)</span></span>
 <span><span class="co">## Platform: x86_64-pc-linux-gnu</span></span>
-<span><span class="co">## Running under: Ubuntu 22.04.3 LTS</span></span>
+<span><span class="co">## Running under: Ubuntu 22.04.4 LTS</span></span>
 <span><span class="co">## </span></span>
 <span><span class="co">## Matrix products: default</span></span>
 <span><span class="co">## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 </span></span>
@@ -2435,92 +2430,90 @@ <h2 id="session-info">Session Info<a class="anchor" aria-label="anchor" href="#s
 <span><span class="co">## </span></span>
 <span><span class="co">## other attached packages:</span></span>
 <span><span class="co">##  [1] openxlsx_4.2.5.2     cowplot_1.1.3        gridExtra_2.3       </span></span>
-<span><span class="co">##  [4] ggplot2_3.4.4        BiocFileCache_2.11.1 dbplyr_2.4.0        </span></span>
-<span><span class="co">##  [7] ropls_1.35.4         pmp_1.15.0           structToolbox_1.15.1</span></span>
-<span><span class="co">## [10] struct_1.15.3        BiocStyle_2.31.0    </span></span>
+<span><span class="co">##  [4] ggplot2_3.5.1        BiocFileCache_2.12.0 dbplyr_2.5.0        </span></span>
+<span><span class="co">##  [7] ropls_1.36.0         pmp_1.16.0           structToolbox_1.17.3</span></span>
+<span><span class="co">## [10] struct_1.16.0        BiocStyle_2.32.1    </span></span>
 <span><span class="co">## </span></span>
 <span><span class="co">## loaded via a namespace (and not attached):</span></span>
-<span><span class="co">##   [1] DBI_1.2.1                   bitops_1.0-7               </span></span>
-<span><span class="co">##   [3] MultiDataSet_1.31.0         rlang_1.1.3                </span></span>
+<span><span class="co">##   [1] DBI_1.2.3                   MultiDataSet_1.32.0        </span></span>
+<span><span class="co">##   [3] httr2_1.0.1                 rlang_1.1.4                </span></span>
 <span><span class="co">##   [5] magrittr_2.0.3              e1071_1.7-14               </span></span>
-<span><span class="co">##   [7] matrixStats_1.2.0           compiler_4.4.0             </span></span>
-<span><span class="co">##   [9] RSQLite_2.3.5               systemfonts_1.0.5          </span></span>
+<span><span class="co">##   [7] RSQLite_2.3.7               matrixStats_1.3.0          </span></span>
+<span><span class="co">##   [9] compiler_4.4.1              systemfonts_1.1.0          </span></span>
 <span><span class="co">##  [11] vctrs_0.6.5                 reshape2_1.4.4             </span></span>
 <span><span class="co">##  [13] stringr_1.5.1               pkgconfig_2.0.3            </span></span>
-<span><span class="co">##  [15] crayon_1.5.2                fastmap_1.1.1              </span></span>
-<span><span class="co">##  [17] XVector_0.43.1              labeling_0.4.3             </span></span>
-<span><span class="co">##  [19] utf8_1.2.4                  rmarkdown_2.25             </span></span>
-<span><span class="co">##  [21] itertools_0.1-3             ragg_1.2.7                 </span></span>
-<span><span class="co">##  [23] bit_4.0.5                   purrr_1.0.2                </span></span>
-<span><span class="co">##  [25] xfun_0.41                   MultiAssayExperiment_1.29.0</span></span>
-<span><span class="co">##  [27] randomForest_4.7-1.1        zlibbioc_1.49.0            </span></span>
-<span><span class="co">##  [29] cachem_1.0.8                GenomeInfoDb_1.39.5        </span></span>
-<span><span class="co">##  [31] jsonlite_1.8.8              progress_1.2.3             </span></span>
-<span><span class="co">##  [33] blob_1.2.4                  highr_0.10                 </span></span>
-<span><span class="co">##  [35] DelayedArray_0.29.1         prettyunits_1.2.0          </span></span>
-<span><span class="co">##  [37] parallel_4.4.0              rols_2.31.0                </span></span>
-<span><span class="co">##  [39] R6_2.5.1                    bslib_0.6.1                </span></span>
-<span><span class="co">##  [41] stringi_1.8.3               limma_3.59.1               </span></span>
-<span><span class="co">##  [43] GenomicRanges_1.55.2        jquerylib_0.1.4            </span></span>
-<span><span class="co">##  [45] Rcpp_1.0.12                 bookdown_0.37              </span></span>
-<span><span class="co">##  [47] SummarizedExperiment_1.33.3 iterators_1.0.14           </span></span>
-<span><span class="co">##  [49] knitr_1.45                  IRanges_2.37.1             </span></span>
-<span><span class="co">##  [51] Matrix_1.6-5                tidyselect_1.2.0           </span></span>
-<span><span class="co">##  [53] abind_1.4-5                 yaml_2.3.8                 </span></span>
-<span><span class="co">##  [55] codetools_0.2-19            curl_5.2.0                 </span></span>
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+<span><span class="co">## [101] sass_0.4.9                  digest_0.6.36              </span></span>
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+<span><span class="co">## [105] farver_2.1.2                htmlwidgets_1.6.4          </span></span>
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+<span><span class="co">## [111] httr_1.4.7                  statmod_1.5.0              </span></span>
+<span><span class="co">## [113] bit64_4.0.5                 qqman_0.1.9                </span></span>
+<span><span class="co">## [115] MASS_7.3-61</span></span></code></pre>
 </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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-<p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
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 <!DOCTYPE html>
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     <a class="navbar-brand me-2" href="index.html">structToolbox</a>
 
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+
+      <h1>Authors and Citation</h1>
     </div>
 
-    <div class="section level2 citation">
+    <div class="section level2">
       <h2>Authors</h2>
-      
+
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           <p><strong>Gavin Rhys Lloyd</strong>. Author, maintainer. <a href="https://orcid.org/0000-0001-7989-6695" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
           </p>
         </li>
         <li>
-          <p><strong>Ralf Johannes Maria Weber</strong>. Author. 
+          <p><strong>Ralf Johannes Maria Weber</strong>. Author.
           </p>
         </li>
       </ul></div>
 
-    <div class="section level2 citation">
+    <div class="section level2">
       <h2 id="citation">Citation</h2>
-      <p><small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/inst/CITATION" class="external-link"><code>inst/CITATION</code></a></small></p>
+      <p><small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/inst/CITATION" class="external-link"><code>inst/CITATION</code></a></small></p>
 
       <p>Lloyd, G., Jankevics, A., and Weber, R. 2020. struct: an R/Bioconductor-based framework for standardized metabolomics data analysis and beyond. Bioinformatics, 36(22-23), p.5551-5552.</p>
       <pre>@Article{,
@@ -75,23 +63,24 @@ <h2 id="citation">Citation</h2>
   url = {https://doi.org/10.1093/bioinformatics/btaa1031},
 }</pre>
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    * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
-   */:root,[data-bs-theme="light"]{--bs-blue: #2c3e50;--bs-indigo: #6610f2;--bs-purple: #6f42c1;--bs-pink: #e83e8c;--bs-red: #e74c3c;--bs-orange: #fd7e14;--bs-yellow: #f39c12;--bs-green: #18bc9c;--bs-teal: #20c997;--bs-cyan: #3498db;--bs-black: #000;--bs-white: #fff;--bs-gray: #95a5a6;--bs-gray-dark: #343a40;--bs-gray-100: #f8f9fa;--bs-gray-200: #ecf0f1;--bs-gray-300: #dee2e6;--bs-gray-400: #ced4da;--bs-gray-500: #b4bcc2;--bs-gray-600: #95a5a6;--bs-gray-700: #7b8a8b;--bs-gray-800: #343a40;--bs-gray-900: #212529;--bs-default: #95a5a6;--bs-primary: #2c3e50;--bs-secondary: #95a5a6;--bs-success: #18bc9c;--bs-info: #3498db;--bs-warning: #f39c12;--bs-danger: #e74c3c;--bs-light: #ecf0f1;--bs-dark: #7b8a8b;--bs-default-rgb: 149,165,166;--bs-primary-rgb: 44,62,80;--bs-secondary-rgb: 149,165,166;--bs-success-rgb: 24,188,156;--bs-info-rgb: 52,152,219;--bs-warning-rgb: 243,156,18;--bs-danger-rgb: 231,76,60;--bs-light-rgb: 236,240,241;--bs-dark-rgb: 123,138,139;--bs-primary-text-emphasis: 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3),.btn-group>:not(.btn-check)+.btn,.btn-group>.btn-group:not(:first-child)>.btn{border-top-left-radius:0;border-bottom-left-radius:0}.dropdown-toggle-split{padding-right:.5625rem;padding-left:.5625rem}.dropdown-toggle-split::after,.dropup .dropdown-toggle-split::after,.dropend .dropdown-toggle-split::after{margin-left:0}.dropstart .dropdown-toggle-split::before{margin-right:0}.btn-sm+.dropdown-toggle-split,.btn-group-sm>.btn+.dropdown-toggle-split{padding-right:.375rem;padding-left:.375rem}.btn-lg+.dropdown-toggle-split,.btn-group-lg>.btn+.dropdown-toggle-split{padding-right:.75rem;padding-left:.75rem}.btn-group-vertical{flex-direction:column;-webkit-flex-direction:column;align-items:flex-start;-webkit-align-items:flex-start;justify-content:center;-webkit-justify-content:center}.btn-group-vertical>.btn,.btn-group-vertical>.btn-group{width:100%}.btn-group-vertical>.btn:not(:first-child),.btn-group-vertical>.btn-group:not(:first-child){margin-top:calc(var(--bs-border-width) * -1)}.btn-group-vertical>.btn:not(:last-child):not(.dropdown-toggle),.btn-group-vertical>.btn-group:not(:last-child)>.btn{border-bottom-right-radius:0;border-bottom-left-radius:0}.btn-group-vertical>.btn~.btn,.btn-group-vertical>.btn-group:not(:first-child)>.btn{border-top-left-radius:0;border-top-right-radius:0}.nav{--bs-nav-link-padding-x: 2rem;--bs-nav-link-padding-y: .5rem;--bs-nav-link-font-weight: ;--bs-nav-link-color: var(--bs-link-color);--bs-nav-link-hover-color: var(--bs-link-hover-color);--bs-nav-link-disabled-color: #95a5a6;display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding-left:0;margin-bottom:0;list-style:none}.nav-link{display:block;padding:var(--bs-nav-link-padding-y) var(--bs-nav-link-padding-x);font-size:var(--bs-nav-link-font-size);font-weight:var(--bs-nav-link-font-weight);color:var(--bs-nav-link-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background:none;border:0;transition:color 0.15s ease-in-out,background-color 0.15s ease-in-out,border-color 0.15s ease-in-out}@media (prefers-reduced-motion: reduce){.nav-link{transition:none}}.nav-link:hover,.nav-link:focus{color:var(--bs-nav-link-hover-color)}.nav-link:focus-visible{outline:0;box-shadow:0 0 0 .25rem rgba(44,62,80,0.25)}.nav-link.disabled,.nav-link:disabled{color:var(--bs-nav-link-disabled-color);pointer-events:none;cursor:default}.nav-tabs{--bs-nav-tabs-border-width: var(--bs-border-width);--bs-nav-tabs-border-color: #ecf0f1;--bs-nav-tabs-border-radius: var(--bs-border-radius);--bs-nav-tabs-link-hover-border-color: var(--bs-secondary-bg) var(--bs-secondary-bg) #ecf0f1;--bs-nav-tabs-link-active-color: var(--bs-emphasis-color);--bs-nav-tabs-link-active-bg: var(--bs-body-bg);--bs-nav-tabs-link-active-border-color: var(--bs-border-color) var(--bs-border-color) var(--bs-body-bg);border-bottom:var(--bs-nav-tabs-border-width) solid var(--bs-nav-tabs-border-color)}.nav-tabs .nav-link{margin-bottom:calc(-1 * var(--bs-nav-tabs-border-width));border:var(--bs-nav-tabs-border-width) solid transparent;border-top-left-radius:var(--bs-nav-tabs-border-radius);border-top-right-radius:var(--bs-nav-tabs-border-radius)}.nav-tabs .nav-link:hover,.nav-tabs .nav-link:focus{isolation:isolate;border-color:var(--bs-nav-tabs-link-hover-border-color)}.nav-tabs .nav-link.active,.nav-tabs .nav-item.show .nav-link{color:var(--bs-nav-tabs-link-active-color);background-color:var(--bs-nav-tabs-link-active-bg);border-color:var(--bs-nav-tabs-link-active-border-color)}.nav-tabs .dropdown-menu{margin-top:calc(-1 * 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992px){.navbar-expand-lg{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-lg .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-lg .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-lg .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-lg .navbar-nav-scroll{overflow:visible}.navbar-expand-lg .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-lg .navbar-toggler{display:none}.navbar-expand-lg .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-lg .offcanvas .offcanvas-header{display:none}.navbar-expand-lg .offcanvas 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.offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex 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stroke='rgba%28255,255,255,0.75%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}[data-bs-theme="dark"] .navbar-toggler-icon{--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgba%28255,255,255,0.75%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}.card{--bs-card-spacer-y: 1rem;--bs-card-spacer-x: 1rem;--bs-card-title-spacer-y: .5rem;--bs-card-title-color: ;--bs-card-subtitle-color: ;--bs-card-border-width: var(--bs-border-width);--bs-card-border-color: var(--bs-border-color-translucent);--bs-card-border-radius: var(--bs-border-radius);--bs-card-box-shadow: ;--bs-card-inner-border-radius: calc(var(--bs-border-radius) - (var(--bs-border-width)));--bs-card-cap-padding-y: .5rem;--bs-card-cap-padding-x: 1rem;--bs-card-cap-bg: rgba(var(--bs-body-color-rgb), 0.03);--bs-card-cap-color: 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ease-in-out,border-color 0.15s ease-in-out,box-shadow 0.15s ease-in-out,border-radius 0.15s ease;--bs-accordion-border-color: var(--bs-border-color);--bs-accordion-border-width: var(--bs-border-width);--bs-accordion-border-radius: var(--bs-border-radius);--bs-accordion-inner-border-radius: calc(var(--bs-border-radius) - (var(--bs-border-width)));--bs-accordion-btn-padding-x: 1.25rem;--bs-accordion-btn-padding-y: 1rem;--bs-accordion-btn-color: var(--bs-body-color);--bs-accordion-btn-bg: var(--bs-accordion-bg);--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23212529'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-icon-width: 1.25rem;--bs-accordion-btn-icon-transform: rotate(-180deg);--bs-accordion-btn-icon-transition: transform 0.2s ease-in-out;--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23121920'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: #969fa8;--bs-accordion-btn-focus-box-shadow: 0 0 0 .25rem rgba(44,62,80,0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: var(--bs-primary-text-emphasis);--bs-accordion-active-bg: var(--bs-primary-bg-subtle)}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;border-radius:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media 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.accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme="dark"] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23808b96'%3e%3cpath 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var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, "/") /* rtl: var(--bs-breadcrumb-divider, "/") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: .75rem;--bs-pagination-padding-y: .375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #fff;--bs-pagination-bg: #18bc9c;--bs-pagination-border-width: 0;--bs-pagination-border-color: rgba(0,0,0,0);--bs-pagination-border-radius: var(--bs-border-radius);--bs-pagination-hover-color: #fff;--bs-pagination-hover-bg: #0f7864;--bs-pagination-hover-border-color: rgba(0,0,0,0);--bs-pagination-focus-color: var(--bs-link-hover-color);--bs-pagination-focus-bg: var(--bs-secondary-bg);--bs-pagination-focus-box-shadow: 0 0 0 .25rem rgba(44,62,80,0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #0f7864;--bs-pagination-active-border-color: rgba(0,0,0,0);--bs-pagination-disabled-color: #ecf0f1;--bs-pagination-disabled-bg: #3be6c4;--bs-pagination-disabled-border-color: rgba(0,0,0,0);display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color 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-1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: .75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: var(--bs-border-radius-lg)}.pagination-sm{--bs-pagination-padding-x: .5rem;--bs-pagination-padding-y: .25rem;--bs-pagination-font-size:.875rem;--bs-pagination-border-radius: var(--bs-border-radius-sm)}.badge{--bs-badge-padding-x: .65em;--bs-badge-padding-y: .35em;--bs-badge-font-size:.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: var(--bs-border-radius);display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: var(--bs-border-width) solid var(--bs-alert-border-color);--bs-alert-border-radius: var(--bs-border-radius);--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:.75rem;--bs-progress-bg: var(--bs-secondary-bg);--bs-progress-border-radius: var(--bs-border-radius);--bs-progress-box-shadow: var(--bs-box-shadow-inset);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #2c3e50;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media (prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media (prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: var(--bs-body-color);--bs-list-group-bg: var(--bs-body-bg);--bs-list-group-border-color: var(--bs-border-color);--bs-list-group-border-width: var(--bs-border-width);--bs-list-group-border-radius: var(--bs-border-radius);--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: .5rem;--bs-list-group-action-color: var(--bs-secondary-color);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: #ecf0f1;--bs-list-group-action-active-color: var(--bs-body-color);--bs-list-group-action-active-bg: var(--bs-secondary-bg);--bs-list-group-disabled-color: var(--bs-secondary-color);--bs-list-group-disabled-bg: #ecf0f1;--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #2c3e50;--bs-list-group-active-border-color: #2c3e50;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0;border-radius:var(--bs-list-group-border-radius)}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") 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";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item:first-child{border-top-left-radius:inherit;border-top-right-radius:inherit}.list-group-item:last-child{border-bottom-right-radius:inherit;border-bottom-left-radius:inherit}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1 * var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media (min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xxl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush{border-radius:0}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #fff;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' 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var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: .5rem;--bs-modal-color: ;--bs-modal-bg: var(--bs-body-bg);--bs-modal-border-color: var(--bs-border-color-translucent);--bs-modal-border-width: var(--bs-border-width);--bs-modal-border-radius: var(--bs-border-radius-lg);--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0,0,0,0.075);--bs-modal-inner-border-radius: calc(var(--bs-border-radius-lg) - (var(--bs-border-width)));--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: var(--bs-border-color);--bs-modal-header-border-width: var(--bs-border-width);--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: .5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: var(--bs-border-color);--bs-modal-footer-border-width: var(--bs-border-width);position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform 0.3s ease-out;transform:translate(0, -50px)}@media (prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static .modal-dialog{transform:scale(1.02)}.modal-dialog-scrollable{height:calc(100% - var(--bs-modal-margin) * 2)}.modal-dialog-scrollable .modal-content{max-height:100%;overflow:hidden}.modal-dialog-scrollable .modal-body{overflow-y:auto}.modal-dialog-centered{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;min-height:calc(100% - var(--bs-modal-margin) * 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3),.btn-group>:not(.btn-check)+.btn,.btn-group>.btn-group:not(:first-child)>.btn{border-top-left-radius:0;border-bottom-left-radius:0}.dropdown-toggle-split{padding-right:.5625rem;padding-left:.5625rem}.dropdown-toggle-split::after,.dropup .dropdown-toggle-split::after,.dropend .dropdown-toggle-split::after{margin-left:0}.dropstart .dropdown-toggle-split::before{margin-right:0}.btn-sm+.dropdown-toggle-split,.btn-group-sm>.btn+.dropdown-toggle-split{padding-right:.375rem;padding-left:.375rem}.btn-lg+.dropdown-toggle-split,.btn-group-lg>.btn+.dropdown-toggle-split{padding-right:.75rem;padding-left:.75rem}.btn-group-vertical{flex-direction:column;-webkit-flex-direction:column;align-items:flex-start;-webkit-align-items:flex-start;justify-content:center;-webkit-justify-content:center}.btn-group-vertical>.btn,.btn-group-vertical>.btn-group{width:100%}.btn-group-vertical>.btn:not(:first-child),.btn-group-vertical>.btn-group:not(:first-child){margin-top:calc(var(--bs-border-width) * -1)}.btn-group-vertical>.btn:not(:last-child):not(.dropdown-toggle),.btn-group-vertical>.btn-group:not(:last-child)>.btn{border-bottom-right-radius:0;border-bottom-left-radius:0}.btn-group-vertical>.btn~.btn,.btn-group-vertical>.btn-group:not(:first-child)>.btn{border-top-left-radius:0;border-top-right-radius:0}.nav{--bs-nav-link-padding-x: 2rem;--bs-nav-link-padding-y: .5rem;--bs-nav-link-font-weight: ;--bs-nav-link-color: var(--bs-link-color);--bs-nav-link-hover-color: var(--bs-link-hover-color);--bs-nav-link-disabled-color: #95a5a6;display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding-left:0;margin-bottom:0;list-style:none}.nav-link{display:block;padding:var(--bs-nav-link-padding-y) var(--bs-nav-link-padding-x);font-size:var(--bs-nav-link-font-size);font-weight:var(--bs-nav-link-font-weight);color:var(--bs-nav-link-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background:none;border:0;transition:color 0.15s ease-in-out,background-color 0.15s ease-in-out,border-color 0.15s ease-in-out}@media (prefers-reduced-motion: reduce){.nav-link{transition:none}}.nav-link:hover,.nav-link:focus{color:var(--bs-nav-link-hover-color)}.nav-link:focus-visible{outline:0;box-shadow:0 0 0 .25rem rgba(44,62,80,0.25)}.nav-link.disabled,.nav-link:disabled{color:var(--bs-nav-link-disabled-color);pointer-events:none;cursor:default}.nav-tabs{--bs-nav-tabs-border-width: var(--bs-border-width);--bs-nav-tabs-border-color: #ecf0f1;--bs-nav-tabs-border-radius: var(--bs-border-radius);--bs-nav-tabs-link-hover-border-color: var(--bs-secondary-bg) var(--bs-secondary-bg) #ecf0f1;--bs-nav-tabs-link-active-color: var(--bs-emphasis-color);--bs-nav-tabs-link-active-bg: var(--bs-body-bg);--bs-nav-tabs-link-active-border-color: var(--bs-border-color) var(--bs-border-color) var(--bs-body-bg);border-bottom:var(--bs-nav-tabs-border-width) solid var(--bs-nav-tabs-border-color)}.nav-tabs .nav-link{margin-bottom:calc(-1 * var(--bs-nav-tabs-border-width));border:var(--bs-nav-tabs-border-width) solid transparent;border-top-left-radius:var(--bs-nav-tabs-border-radius);border-top-right-radius:var(--bs-nav-tabs-border-radius)}.nav-tabs .nav-link:hover,.nav-tabs .nav-link:focus{isolation:isolate;border-color:var(--bs-nav-tabs-link-hover-border-color)}.nav-tabs .nav-link.active,.nav-tabs .nav-item.show .nav-link{color:var(--bs-nav-tabs-link-active-color);background-color:var(--bs-nav-tabs-link-active-bg);border-color:var(--bs-nav-tabs-link-active-border-color)}.nav-tabs .dropdown-menu{margin-top:calc(-1 * 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992px){.navbar-expand-lg{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-lg .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-lg .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-lg .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-lg .navbar-nav-scroll{overflow:visible}.navbar-expand-lg .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-lg .navbar-toggler{display:none}.navbar-expand-lg .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-lg .offcanvas .offcanvas-header{display:none}.navbar-expand-lg .offcanvas 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.offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex 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stroke='rgba%28255,255,255,0.75%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}[data-bs-theme="dark"] .navbar-toggler-icon{--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgba%28255,255,255,0.75%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}.card{--bs-card-spacer-y: 1rem;--bs-card-spacer-x: 1rem;--bs-card-title-spacer-y: .5rem;--bs-card-title-color: ;--bs-card-subtitle-color: ;--bs-card-border-width: var(--bs-border-width);--bs-card-border-color: var(--bs-border-color-translucent);--bs-card-border-radius: var(--bs-border-radius);--bs-card-box-shadow: ;--bs-card-inner-border-radius: calc(var(--bs-border-radius) - (var(--bs-border-width)));--bs-card-cap-padding-y: .5rem;--bs-card-cap-padding-x: 1rem;--bs-card-cap-bg: rgba(var(--bs-body-color-rgb), 0.03);--bs-card-cap-color: 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ease-in-out,border-color 0.15s ease-in-out,box-shadow 0.15s ease-in-out,border-radius 0.15s ease;--bs-accordion-border-color: var(--bs-border-color);--bs-accordion-border-width: var(--bs-border-width);--bs-accordion-border-radius: var(--bs-border-radius);--bs-accordion-inner-border-radius: calc(var(--bs-border-radius) - (var(--bs-border-width)));--bs-accordion-btn-padding-x: 1.25rem;--bs-accordion-btn-padding-y: 1rem;--bs-accordion-btn-color: var(--bs-body-color);--bs-accordion-btn-bg: var(--bs-accordion-bg);--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23212529'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-icon-width: 1.25rem;--bs-accordion-btn-icon-transform: rotate(-180deg);--bs-accordion-btn-icon-transition: transform 0.2s ease-in-out;--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23121920'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: #969fa8;--bs-accordion-btn-focus-box-shadow: 0 0 0 .25rem rgba(44,62,80,0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: var(--bs-primary-text-emphasis);--bs-accordion-active-bg: var(--bs-primary-bg-subtle)}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;border-radius:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media 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.accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme="dark"] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23808b96'%3e%3cpath 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var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, "/") /* rtl: var(--bs-breadcrumb-divider, "/") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: .75rem;--bs-pagination-padding-y: .375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #fff;--bs-pagination-bg: #18bc9c;--bs-pagination-border-width: 0;--bs-pagination-border-color: rgba(0,0,0,0);--bs-pagination-border-radius: var(--bs-border-radius);--bs-pagination-hover-color: #fff;--bs-pagination-hover-bg: #0f7864;--bs-pagination-hover-border-color: rgba(0,0,0,0);--bs-pagination-focus-color: var(--bs-link-hover-color);--bs-pagination-focus-bg: var(--bs-secondary-bg);--bs-pagination-focus-box-shadow: 0 0 0 .25rem rgba(44,62,80,0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #0f7864;--bs-pagination-active-border-color: rgba(0,0,0,0);--bs-pagination-disabled-color: #ecf0f1;--bs-pagination-disabled-bg: #3be6c4;--bs-pagination-disabled-border-color: rgba(0,0,0,0);display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color 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-1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: .75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: var(--bs-border-radius-lg)}.pagination-sm{--bs-pagination-padding-x: .5rem;--bs-pagination-padding-y: .25rem;--bs-pagination-font-size:.875rem;--bs-pagination-border-radius: var(--bs-border-radius-sm)}.badge{--bs-badge-padding-x: .65em;--bs-badge-padding-y: .35em;--bs-badge-font-size:.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: var(--bs-border-radius);display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: var(--bs-border-width) solid var(--bs-alert-border-color);--bs-alert-border-radius: var(--bs-border-radius);--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:.75rem;--bs-progress-bg: var(--bs-secondary-bg);--bs-progress-border-radius: var(--bs-border-radius);--bs-progress-box-shadow: var(--bs-box-shadow-inset);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #2c3e50;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media (prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media (prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: var(--bs-body-color);--bs-list-group-bg: var(--bs-body-bg);--bs-list-group-border-color: var(--bs-border-color);--bs-list-group-border-width: var(--bs-border-width);--bs-list-group-border-radius: var(--bs-border-radius);--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: .5rem;--bs-list-group-action-color: var(--bs-secondary-color);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: #ecf0f1;--bs-list-group-action-active-color: var(--bs-body-color);--bs-list-group-action-active-bg: var(--bs-secondary-bg);--bs-list-group-disabled-color: var(--bs-secondary-color);--bs-list-group-disabled-bg: #ecf0f1;--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #2c3e50;--bs-list-group-active-border-color: #2c3e50;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0;border-radius:var(--bs-list-group-border-radius)}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") 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";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item:first-child{border-top-left-radius:inherit;border-top-right-radius:inherit}.list-group-item:last-child{border-bottom-right-radius:inherit;border-bottom-left-radius:inherit}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1 * var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media (min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xxl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush{border-radius:0}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #fff;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' 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var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: .5rem;--bs-modal-color: ;--bs-modal-bg: var(--bs-body-bg);--bs-modal-border-color: var(--bs-border-color-translucent);--bs-modal-border-width: var(--bs-border-width);--bs-modal-border-radius: var(--bs-border-radius-lg);--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0,0,0,0.075);--bs-modal-inner-border-radius: calc(var(--bs-border-radius-lg) - (var(--bs-border-width)));--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: var(--bs-border-color);--bs-modal-header-border-width: var(--bs-border-width);--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: .5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: var(--bs-border-color);--bs-modal-footer-border-width: var(--bs-border-width);position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform 0.3s ease-out;transform:translate(0, -50px)}@media (prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static .modal-dialog{transform:scale(1.02)}.modal-dialog-scrollable{height:calc(100% - var(--bs-modal-margin) * 2)}.modal-dialog-scrollable .modal-content{max-height:100%;overflow:hidden}.modal-dialog-scrollable .modal-body{overflow-y:auto}.modal-dialog-centered{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;min-height:calc(100% - var(--bs-modal-margin) * 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diff --git a/deps/bootstrap-5.3.1/font.css b/deps/bootstrap-5.3.1/font.css
index 409df22..02b33a1 100644
--- a/deps/bootstrap-5.3.1/font.css
+++ b/deps/bootstrap-5.3.1/font.css
@@ -1,21 +1,54 @@
+/* latin-ext */
 @font-face {
   font-family: 'Lato';
   font-style: italic;
   font-weight: 400;
   font-display: swap;
-  src: url(fonts/S6u8w4BMUTPHjxswWA.woff) format('woff');
+  src: url(fonts/S6u8w4BMUTPHjxsAUi-qJCY.woff2) format('woff2');
+  unicode-range: U+0100-02AF, U+0304, U+0308, U+0329, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, U+A720-A7FF;
 }
+/* latin */
+@font-face {
+  font-family: 'Lato';
+  font-style: italic;
+  font-weight: 400;
+  font-display: swap;
+  src: url(fonts/S6u8w4BMUTPHjxsAXC-q.woff2) format('woff2');
+  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, U+2000-206F, U+2074, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
+}
+/* latin-ext */
+@font-face {
+  font-family: 'Lato';
+  font-style: normal;
+  font-weight: 400;
+  font-display: swap;
+  src: url(fonts/S6uyw4BMUTPHjxAwXjeu.woff2) format('woff2');
+  unicode-range: U+0100-02AF, U+0304, U+0308, U+0329, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, U+A720-A7FF;
+}
+/* latin */
 @font-face {
   font-family: 'Lato';
   font-style: normal;
   font-weight: 400;
   font-display: swap;
-  src: url(fonts/S6uyw4BMUTPHvxo.woff) format('woff');
+  src: url(fonts/S6uyw4BMUTPHjx4wXg.woff2) format('woff2');
+  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, U+2000-206F, U+2074, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
+}
+/* latin-ext */
+@font-face {
+  font-family: 'Lato';
+  font-style: normal;
+  font-weight: 700;
+  font-display: swap;
+  src: url(fonts/S6u9w4BMUTPHh6UVSwaPGR_p.woff2) format('woff2');
+  unicode-range: U+0100-02AF, U+0304, U+0308, U+0329, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, U+A720-A7FF;
 }
+/* latin */
 @font-face {
   font-family: 'Lato';
   font-style: normal;
   font-weight: 700;
   font-display: swap;
-  src: url(fonts/S6u9w4BMUTPHh6UVeww.woff) format('woff');
+  src: url(fonts/S6u9w4BMUTPHh6UVSwiPGQ.woff2) format('woff2');
+  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, U+2000-206F, U+2074, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
 }
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index 693b82d..8aa49e2 100644
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+.fa-sharp,
+.fas,
+.fa-solid,
+.far,
+.fa-regular,
+.fab,
+.fa-brands {
+  -moz-osx-font-smoothing: grayscale;
+  -webkit-font-smoothing: antialiased;
+  display: var(--fa-display, inline-block);
+  font-style: normal;
+  font-variant: normal;
+  line-height: 1;
+  text-rendering: auto; }
+
+.fas,
+.fa-classic,
+.fa-solid,
+.far,
+.fa-regular {
+  font-family: 'Font Awesome 6 Free'; }
+
+.fab,
+.fa-brands {
+  font-family: 'Font Awesome 6 Brands'; }
+
+.fa-1x {
+  font-size: 1em; }
+
+.fa-2x {
+  font-size: 2em; }
+
+.fa-3x {
+  font-size: 3em; }
+
+.fa-4x {
+  font-size: 4em; }
+
+.fa-5x {
+  font-size: 5em; }
+
+.fa-6x {
+  font-size: 6em; }
+
+.fa-7x {
+  font-size: 7em; }
+
+.fa-8x {
+  font-size: 8em; }
+
+.fa-9x {
+  font-size: 9em; }
+
+.fa-10x {
+  font-size: 10em; }
+
+.fa-2xs {
+  font-size: 0.625em;
+  line-height: 0.1em;
+  vertical-align: 0.225em; }
+
+.fa-xs {
+  font-size: 0.75em;
+  line-height: 0.08333em;
+  vertical-align: 0.125em; }
+
+.fa-sm {
+  font-size: 0.875em;
+  line-height: 0.07143em;
+  vertical-align: 0.05357em; }
+
+.fa-lg {
+  font-size: 1.25em;
+  line-height: 0.05em;
+  vertical-align: -0.075em; }
+
+.fa-xl {
+  font-size: 1.5em;
+  line-height: 0.04167em;
+  vertical-align: -0.125em; }
+
+.fa-2xl {
+  font-size: 2em;
+  line-height: 0.03125em;
+  vertical-align: -0.1875em; }
+
+.fa-fw {
+  text-align: center;
+  width: 1.25em; }
+
+.fa-ul {
+  list-style-type: none;
+  margin-left: var(--fa-li-margin, 2.5em);
+  padding-left: 0; }
+  .fa-ul > li {
+    position: relative; }
+
+.fa-li {
+  left: calc(var(--fa-li-width, 2em) * -1);
+  position: absolute;
+  text-align: center;
+  width: var(--fa-li-width, 2em);
+  line-height: inherit; }
+
+.fa-border {
+  border-color: var(--fa-border-color, #eee);
+  border-radius: var(--fa-border-radius, 0.1em);
+  border-style: var(--fa-border-style, solid);
+  border-width: var(--fa-border-width, 0.08em);
+  padding: var(--fa-border-padding, 0.2em 0.25em 0.15em); }
+
+.fa-pull-left {
+  float: left;
+  margin-right: var(--fa-pull-margin, 0.3em); }
+
+.fa-pull-right {
+  float: right;
+  margin-left: var(--fa-pull-margin, 0.3em); }
+
+.fa-beat {
+  -webkit-animation-name: fa-beat;
+          animation-name: fa-beat;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, ease-in-out);
+          animation-timing-function: var(--fa-animation-timing, ease-in-out); }
+
+.fa-bounce {
+  -webkit-animation-name: fa-bounce;
+          animation-name: fa-bounce;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.28, 0.84, 0.42, 1));
+          animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.28, 0.84, 0.42, 1)); }
+
+.fa-fade {
+  -webkit-animation-name: fa-fade;
+          animation-name: fa-fade;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1));
+          animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); }
+
+.fa-beat-fade {
+  -webkit-animation-name: fa-beat-fade;
+          animation-name: fa-beat-fade;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1));
+          animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); }
+
+.fa-flip {
+  -webkit-animation-name: fa-flip;
+          animation-name: fa-flip;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, ease-in-out);
+          animation-timing-function: var(--fa-animation-timing, ease-in-out); }
+
+.fa-shake {
+  -webkit-animation-name: fa-shake;
+          animation-name: fa-shake;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, linear);
+          animation-timing-function: var(--fa-animation-timing, linear); }
+
+.fa-spin {
+  -webkit-animation-name: fa-spin;
+          animation-name: fa-spin;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 2s);
+          animation-duration: var(--fa-animation-duration, 2s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, linear);
+          animation-timing-function: var(--fa-animation-timing, linear); }
+
+.fa-spin-reverse {
+  --fa-animation-direction: reverse; }
+
+.fa-pulse,
+.fa-spin-pulse {
+  -webkit-animation-name: fa-spin;
+          animation-name: fa-spin;
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, steps(8));
+          animation-timing-function: var(--fa-animation-timing, steps(8)); }
+
+@media (prefers-reduced-motion: reduce) {
+  .fa-beat,
+  .fa-bounce,
+  .fa-fade,
+  .fa-beat-fade,
+  .fa-flip,
+  .fa-pulse,
+  .fa-shake,
+  .fa-spin,
+  .fa-spin-pulse {
+    -webkit-animation-delay: -1ms;
+            animation-delay: -1ms;
+    -webkit-animation-duration: 1ms;
+            animation-duration: 1ms;
+    -webkit-animation-iteration-count: 1;
+            animation-iteration-count: 1;
+    -webkit-transition-delay: 0s;
+            transition-delay: 0s;
+    -webkit-transition-duration: 0s;
+            transition-duration: 0s; } }
+
+@-webkit-keyframes fa-beat {
+  0%, 90% {
+    -webkit-transform: scale(1);
+            transform: scale(1); }
+  45% {
+    -webkit-transform: scale(var(--fa-beat-scale, 1.25));
+            transform: scale(var(--fa-beat-scale, 1.25)); } }
+
+@keyframes fa-beat {
+  0%, 90% {
+    -webkit-transform: scale(1);
+            transform: scale(1); }
+  45% {
+    -webkit-transform: scale(var(--fa-beat-scale, 1.25));
+            transform: scale(var(--fa-beat-scale, 1.25)); } }
+
+@-webkit-keyframes fa-bounce {
+  0% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); }
+  10% {
+    -webkit-transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0);
+            transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); }
+  30% {
+    -webkit-transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em));
+            transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); }
+  50% {
+    -webkit-transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0);
+            transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); }
+  57% {
+    -webkit-transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em));
+            transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); }
+  64% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); }
+  100% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); } }
+
+@keyframes fa-bounce {
+  0% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); }
+  10% {
+    -webkit-transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0);
+            transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); }
+  30% {
+    -webkit-transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em));
+            transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); }
+  50% {
+    -webkit-transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0);
+            transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); }
+  57% {
+    -webkit-transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em));
+            transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); }
+  64% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); }
+  100% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); } }
+
+@-webkit-keyframes fa-fade {
+  50% {
+    opacity: var(--fa-fade-opacity, 0.4); } }
+
+@keyframes fa-fade {
+  50% {
+    opacity: var(--fa-fade-opacity, 0.4); } }
+
+@-webkit-keyframes fa-beat-fade {
+  0%, 100% {
+    opacity: var(--fa-beat-fade-opacity, 0.4);
+    -webkit-transform: scale(1);
+            transform: scale(1); }
+  50% {
+    opacity: 1;
+    -webkit-transform: scale(var(--fa-beat-fade-scale, 1.125));
+            transform: scale(var(--fa-beat-fade-scale, 1.125)); } }
+
+@keyframes fa-beat-fade {
+  0%, 100% {
+    opacity: var(--fa-beat-fade-opacity, 0.4);
+    -webkit-transform: scale(1);
+            transform: scale(1); }
+  50% {
+    opacity: 1;
+    -webkit-transform: scale(var(--fa-beat-fade-scale, 1.125));
+            transform: scale(var(--fa-beat-fade-scale, 1.125)); } }
+
+@-webkit-keyframes fa-flip {
+  50% {
+    -webkit-transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg));
+            transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); } }
+
+@keyframes fa-flip {
+  50% {
+    -webkit-transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg));
+            transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); } }
+
+@-webkit-keyframes fa-shake {
+  0% {
+    -webkit-transform: rotate(-15deg);
+            transform: rotate(-15deg); }
+  4% {
+    -webkit-transform: rotate(15deg);
+            transform: rotate(15deg); }
+  8%, 24% {
+    -webkit-transform: rotate(-18deg);
+            transform: rotate(-18deg); }
+  12%, 28% {
+    -webkit-transform: rotate(18deg);
+            transform: rotate(18deg); }
+  16% {
+    -webkit-transform: rotate(-22deg);
+            transform: rotate(-22deg); }
+  20% {
+    -webkit-transform: rotate(22deg);
+            transform: rotate(22deg); }
+  32% {
+    -webkit-transform: rotate(-12deg);
+            transform: rotate(-12deg); }
+  36% {
+    -webkit-transform: rotate(12deg);
+            transform: rotate(12deg); }
+  40%, 100% {
+    -webkit-transform: rotate(0deg);
+            transform: rotate(0deg); } }
+
+@keyframes fa-shake {
+  0% {
+    -webkit-transform: rotate(-15deg);
+            transform: rotate(-15deg); }
+  4% {
+    -webkit-transform: rotate(15deg);
+            transform: rotate(15deg); }
+  8%, 24% {
+    -webkit-transform: rotate(-18deg);
+            transform: rotate(-18deg); }
+  12%, 28% {
+    -webkit-transform: rotate(18deg);
+            transform: rotate(18deg); }
+  16% {
+    -webkit-transform: rotate(-22deg);
+            transform: rotate(-22deg); }
+  20% {
+    -webkit-transform: rotate(22deg);
+            transform: rotate(22deg); }
+  32% {
+    -webkit-transform: rotate(-12deg);
+            transform: rotate(-12deg); }
+  36% {
+    -webkit-transform: rotate(12deg);
+            transform: rotate(12deg); }
+  40%, 100% {
+    -webkit-transform: rotate(0deg);
+            transform: rotate(0deg); } }
+
+@-webkit-keyframes fa-spin {
+  0% {
+    -webkit-transform: rotate(0deg);
+            transform: rotate(0deg); }
+  100% {
+    -webkit-transform: rotate(360deg);
+            transform: rotate(360deg); } }
+
+@keyframes fa-spin {
+  0% {
+    -webkit-transform: rotate(0deg);
+            transform: rotate(0deg); }
+  100% {
+    -webkit-transform: rotate(360deg);
+            transform: rotate(360deg); } }
+
+.fa-rotate-90 {
+  -webkit-transform: rotate(90deg);
+          transform: rotate(90deg); }
+
+.fa-rotate-180 {
+  -webkit-transform: rotate(180deg);
+          transform: rotate(180deg); }
+
+.fa-rotate-270 {
+  -webkit-transform: rotate(270deg);
+          transform: rotate(270deg); }
+
+.fa-flip-horizontal {
+  -webkit-transform: scale(-1, 1);
+          transform: scale(-1, 1); }
+
+.fa-flip-vertical {
+  -webkit-transform: scale(1, -1);
+          transform: scale(1, -1); }
+
+.fa-flip-both,
+.fa-flip-horizontal.fa-flip-vertical {
+  -webkit-transform: scale(-1, -1);
+          transform: scale(-1, -1); }
+
+.fa-rotate-by {
+  -webkit-transform: rotate(var(--fa-rotate-angle, none));
+          transform: rotate(var(--fa-rotate-angle, none)); }
+
+.fa-stack {
+  display: inline-block;
+  height: 2em;
+  line-height: 2em;
+  position: relative;
+  vertical-align: middle;
+  width: 2.5em; }
+
+.fa-stack-1x,
+.fa-stack-2x {
+  left: 0;
+  position: absolute;
+  text-align: center;
+  width: 100%;
+  z-index: var(--fa-stack-z-index, auto); }
+
+.fa-stack-1x {
+  line-height: inherit; }
+
+.fa-stack-2x {
+  font-size: 2em; }
+
+.fa-inverse {
+  color: var(--fa-inverse, #fff); }
+
+/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
+readers do not read off random characters that represent icons */
+
+.fa-0::before {
+  content: "\30"; }
+
+.fa-1::before {
+  content: "\31"; }
+
+.fa-2::before {
+  content: "\32"; }
+
+.fa-3::before {
+  content: "\33"; }
+
+.fa-4::before {
+  content: "\34"; }
+
+.fa-5::before {
+  content: "\35"; }
+
+.fa-6::before {
+  content: "\36"; }
+
+.fa-7::before {
+  content: "\37"; }
+
+.fa-8::before {
+  content: "\38"; }
+
+.fa-9::before {
+  content: "\39"; }
+
+.fa-fill-drip::before {
+  content: "\f576"; }
+
+.fa-arrows-to-circle::before {
+  content: "\e4bd"; }
+
+.fa-circle-chevron-right::before {
+  content: "\f138"; }
+
+.fa-chevron-circle-right::before {
+  content: "\f138"; }
+
+.fa-at::before {
+  content: "\40"; }
+
+.fa-trash-can::before {
+  content: "\f2ed"; }
+
+.fa-trash-alt::before {
+  content: "\f2ed"; }
+
+.fa-text-height::before {
+  content: "\f034"; }
+
+.fa-user-xmark::before {
+  content: "\f235"; }
+
+.fa-user-times::before {
+  content: "\f235"; }
+
+.fa-stethoscope::before {
+  content: "\f0f1"; }
+
+.fa-message::before {
+  content: "\f27a"; }
+
+.fa-comment-alt::before {
+  content: "\f27a"; }
+
+.fa-info::before {
+  content: "\f129"; }
+
+.fa-down-left-and-up-right-to-center::before {
+  content: "\f422"; }
+
+.fa-compress-alt::before {
+  content: "\f422"; }
+
+.fa-explosion::before {
+  content: "\e4e9"; }
+
+.fa-file-lines::before {
+  content: "\f15c"; }
+
+.fa-file-alt::before {
+  content: "\f15c"; }
+
+.fa-file-text::before {
+  content: "\f15c"; }
+
+.fa-wave-square::before {
+  content: "\f83e"; }
+
+.fa-ring::before {
+  content: "\f70b"; }
+
+.fa-building-un::before {
+  content: "\e4d9"; }
+
+.fa-dice-three::before {
+  content: "\f527"; }
+
+.fa-calendar-days::before {
+  content: "\f073"; }
+
+.fa-calendar-alt::before {
+  content: "\f073"; }
+
+.fa-anchor-circle-check::before {
+  content: "\e4aa"; }
+
+.fa-building-circle-arrow-right::before {
+  content: "\e4d1"; }
+
+.fa-volleyball::before {
+  content: "\f45f"; }
+
+.fa-volleyball-ball::before {
+  content: "\f45f"; }
+
+.fa-arrows-up-to-line::before {
+  content: "\e4c2"; }
+
+.fa-sort-down::before {
+  content: "\f0dd"; }
+
+.fa-sort-desc::before {
+  content: "\f0dd"; }
+
+.fa-circle-minus::before {
+  content: "\f056"; }
+
+.fa-minus-circle::before {
+  content: "\f056"; }
+
+.fa-door-open::before {
+  content: "\f52b"; }
+
+.fa-right-from-bracket::before {
+  content: "\f2f5"; }
+
+.fa-sign-out-alt::before {
+  content: "\f2f5"; }
+
+.fa-atom::before {
+  content: "\f5d2"; }
+
+.fa-soap::before {
+  content: "\e06e"; }
+
+.fa-icons::before {
+  content: "\f86d"; }
+
+.fa-heart-music-camera-bolt::before {
+  content: "\f86d"; }
+
+.fa-microphone-lines-slash::before {
+  content: "\f539"; }
+
+.fa-microphone-alt-slash::before {
+  content: "\f539"; }
+
+.fa-bridge-circle-check::before {
+  content: "\e4c9"; }
+
+.fa-pump-medical::before {
+  content: "\e06a"; }
+
+.fa-fingerprint::before {
+  content: "\f577"; }
+
+.fa-hand-point-right::before {
+  content: "\f0a4"; }
+
+.fa-magnifying-glass-location::before {
+  content: "\f689"; }
+
+.fa-search-location::before {
+  content: "\f689"; }
+
+.fa-forward-step::before {
+  content: "\f051"; }
+
+.fa-step-forward::before {
+  content: "\f051"; }
+
+.fa-face-smile-beam::before {
+  content: "\f5b8"; }
+
+.fa-smile-beam::before {
+  content: "\f5b8"; }
+
+.fa-flag-checkered::before {
+  content: "\f11e"; }
+
+.fa-football::before {
+  content: "\f44e"; }
+
+.fa-football-ball::before {
+  content: "\f44e"; }
+
+.fa-school-circle-exclamation::before {
+  content: "\e56c"; }
+
+.fa-crop::before {
+  content: "\f125"; }
+
+.fa-angles-down::before {
+  content: "\f103"; }
+
+.fa-angle-double-down::before {
+  content: "\f103"; }
+
+.fa-users-rectangle::before {
+  content: "\e594"; }
+
+.fa-people-roof::before {
+  content: "\e537"; }
+
+.fa-people-line::before {
+  content: "\e534"; }
+
+.fa-beer-mug-empty::before {
+  content: "\f0fc"; }
+
+.fa-beer::before {
+  content: "\f0fc"; }
+
+.fa-diagram-predecessor::before {
+  content: "\e477"; }
+
+.fa-arrow-up-long::before {
+  content: "\f176"; }
+
+.fa-long-arrow-up::before {
+  content: "\f176"; }
+
+.fa-fire-flame-simple::before {
+  content: "\f46a"; }
+
+.fa-burn::before {
+  content: "\f46a"; }
+
+.fa-person::before {
+  content: "\f183"; }
+
+.fa-male::before {
+  content: "\f183"; }
+
+.fa-laptop::before {
+  content: "\f109"; }
+
+.fa-file-csv::before {
+  content: "\f6dd"; }
+
+.fa-menorah::before {
+  content: "\f676"; }
+
+.fa-truck-plane::before {
+  content: "\e58f"; }
+
+.fa-record-vinyl::before {
+  content: "\f8d9"; }
+
+.fa-face-grin-stars::before {
+  content: "\f587"; }
+
+.fa-grin-stars::before {
+  content: "\f587"; }
+
+.fa-bong::before {
+  content: "\f55c"; }
+
+.fa-spaghetti-monster-flying::before {
+  content: "\f67b"; }
+
+.fa-pastafarianism::before {
+  content: "\f67b"; }
+
+.fa-arrow-down-up-across-line::before {
+  content: "\e4af"; }
+
+.fa-spoon::before {
+  content: "\f2e5"; }
+
+.fa-utensil-spoon::before {
+  content: "\f2e5"; }
+
+.fa-jar-wheat::before {
+  content: "\e517"; }
+
+.fa-envelopes-bulk::before {
+  content: "\f674"; }
+
+.fa-mail-bulk::before {
+  content: "\f674"; }
+
+.fa-file-circle-exclamation::before {
+  content: "\e4eb"; }
+
+.fa-circle-h::before {
+  content: "\f47e"; }
+
+.fa-hospital-symbol::before {
+  content: "\f47e"; }
+
+.fa-pager::before {
+  content: "\f815"; }
+
+.fa-address-book::before {
+  content: "\f2b9"; }
+
+.fa-contact-book::before {
+  content: "\f2b9"; }
+
+.fa-strikethrough::before {
+  content: "\f0cc"; }
+
+.fa-k::before {
+  content: "\4b"; }
+
+.fa-landmark-flag::before {
+  content: "\e51c"; }
+
+.fa-pencil::before {
+  content: "\f303"; }
+
+.fa-pencil-alt::before {
+  content: "\f303"; }
+
+.fa-backward::before {
+  content: "\f04a"; }
+
+.fa-caret-right::before {
+  content: "\f0da"; }
+
+.fa-comments::before {
+  content: "\f086"; }
+
+.fa-paste::before {
+  content: "\f0ea"; }
+
+.fa-file-clipboard::before {
+  content: "\f0ea"; }
+
+.fa-code-pull-request::before {
+  content: "\e13c"; }
+
+.fa-clipboard-list::before {
+  content: "\f46d"; }
+
+.fa-truck-ramp-box::before {
+  content: "\f4de"; }
+
+.fa-truck-loading::before {
+  content: "\f4de"; }
+
+.fa-user-check::before {
+  content: "\f4fc"; }
+
+.fa-vial-virus::before {
+  content: "\e597"; }
+
+.fa-sheet-plastic::before {
+  content: "\e571"; }
+
+.fa-blog::before {
+  content: "\f781"; }
+
+.fa-user-ninja::before {
+  content: "\f504"; }
+
+.fa-person-arrow-up-from-line::before {
+  content: "\e539"; }
+
+.fa-scroll-torah::before {
+  content: "\f6a0"; }
+
+.fa-torah::before {
+  content: "\f6a0"; }
+
+.fa-broom-ball::before {
+  content: "\f458"; }
+
+.fa-quidditch::before {
+  content: "\f458"; }
+
+.fa-quidditch-broom-ball::before {
+  content: "\f458"; }
+
+.fa-toggle-off::before {
+  content: "\f204"; }
+
+.fa-box-archive::before {
+  content: "\f187"; }
+
+.fa-archive::before {
+  content: "\f187"; }
+
+.fa-person-drowning::before {
+  content: "\e545"; }
+
+.fa-arrow-down-9-1::before {
+  content: "\f886"; }
+
+.fa-sort-numeric-desc::before {
+  content: "\f886"; }
+
+.fa-sort-numeric-down-alt::before {
+  content: "\f886"; }
+
+.fa-face-grin-tongue-squint::before {
+  content: "\f58a"; }
+
+.fa-grin-tongue-squint::before {
+  content: "\f58a"; }
+
+.fa-spray-can::before {
+  content: "\f5bd"; }
+
+.fa-truck-monster::before {
+  content: "\f63b"; }
+
+.fa-w::before {
+  content: "\57"; }
+
+.fa-earth-africa::before {
+  content: "\f57c"; }
+
+.fa-globe-africa::before {
+  content: "\f57c"; }
+
+.fa-rainbow::before {
+  content: "\f75b"; }
+
+.fa-circle-notch::before {
+  content: "\f1ce"; }
+
+.fa-tablet-screen-button::before {
+  content: "\f3fa"; }
+
+.fa-tablet-alt::before {
+  content: "\f3fa"; }
+
+.fa-paw::before {
+  content: "\f1b0"; }
+
+.fa-cloud::before {
+  content: "\f0c2"; }
+
+.fa-trowel-bricks::before {
+  content: "\e58a"; }
+
+.fa-face-flushed::before {
+  content: "\f579"; }
+
+.fa-flushed::before {
+  content: "\f579"; }
+
+.fa-hospital-user::before {
+  content: "\f80d"; }
+
+.fa-tent-arrow-left-right::before {
+  content: "\e57f"; }
+
+.fa-gavel::before {
+  content: "\f0e3"; }
+
+.fa-legal::before {
+  content: "\f0e3"; }
+
+.fa-binoculars::before {
+  content: "\f1e5"; }
+
+.fa-microphone-slash::before {
+  content: "\f131"; }
+
+.fa-box-tissue::before {
+  content: "\e05b"; }
+
+.fa-motorcycle::before {
+  content: "\f21c"; }
+
+.fa-bell-concierge::before {
+  content: "\f562"; }
+
+.fa-concierge-bell::before {
+  content: "\f562"; }
+
+.fa-pen-ruler::before {
+  content: "\f5ae"; }
+
+.fa-pencil-ruler::before {
+  content: "\f5ae"; }
+
+.fa-people-arrows::before {
+  content: "\e068"; }
+
+.fa-people-arrows-left-right::before {
+  content: "\e068"; }
+
+.fa-mars-and-venus-burst::before {
+  content: "\e523"; }
+
+.fa-square-caret-right::before {
+  content: "\f152"; }
+
+.fa-caret-square-right::before {
+  content: "\f152"; }
+
+.fa-scissors::before {
+  content: "\f0c4"; }
+
+.fa-cut::before {
+  content: "\f0c4"; }
+
+.fa-sun-plant-wilt::before {
+  content: "\e57a"; }
+
+.fa-toilets-portable::before {
+  content: "\e584"; }
+
+.fa-hockey-puck::before {
+  content: "\f453"; }
+
+.fa-table::before {
+  content: "\f0ce"; }
+
+.fa-magnifying-glass-arrow-right::before {
+  content: "\e521"; }
+
+.fa-tachograph-digital::before {
+  content: "\f566"; }
+
+.fa-digital-tachograph::before {
+  content: "\f566"; }
+
+.fa-users-slash::before {
+  content: "\e073"; }
+
+.fa-clover::before {
+  content: "\e139"; }
+
+.fa-reply::before {
+  content: "\f3e5"; }
+
+.fa-mail-reply::before {
+  content: "\f3e5"; }
+
+.fa-star-and-crescent::before {
+  content: "\f699"; }
+
+.fa-house-fire::before {
+  content: "\e50c"; }
+
+.fa-square-minus::before {
+  content: "\f146"; }
+
+.fa-minus-square::before {
+  content: "\f146"; }
+
+.fa-helicopter::before {
+  content: "\f533"; }
+
+.fa-compass::before {
+  content: "\f14e"; }
+
+.fa-square-caret-down::before {
+  content: "\f150"; }
+
+.fa-caret-square-down::before {
+  content: "\f150"; }
+
+.fa-file-circle-question::before {
+  content: "\e4ef"; }
+
+.fa-laptop-code::before {
+  content: "\f5fc"; }
+
+.fa-swatchbook::before {
+  content: "\f5c3"; }
+
+.fa-prescription-bottle::before {
+  content: "\f485"; }
+
+.fa-bars::before {
+  content: "\f0c9"; }
+
+.fa-navicon::before {
+  content: "\f0c9"; }
+
+.fa-people-group::before {
+  content: "\e533"; }
+
+.fa-hourglass-end::before {
+  content: "\f253"; }
+
+.fa-hourglass-3::before {
+  content: "\f253"; }
+
+.fa-heart-crack::before {
+  content: "\f7a9"; }
+
+.fa-heart-broken::before {
+  content: "\f7a9"; }
+
+.fa-square-up-right::before {
+  content: "\f360"; }
+
+.fa-external-link-square-alt::before {
+  content: "\f360"; }
+
+.fa-face-kiss-beam::before {
+  content: "\f597"; }
+
+.fa-kiss-beam::before {
+  content: "\f597"; }
+
+.fa-film::before {
+  content: "\f008"; }
+
+.fa-ruler-horizontal::before {
+  content: "\f547"; }
+
+.fa-people-robbery::before {
+  content: "\e536"; }
+
+.fa-lightbulb::before {
+  content: "\f0eb"; }
+
+.fa-caret-left::before {
+  content: "\f0d9"; }
+
+.fa-circle-exclamation::before {
+  content: "\f06a"; }
+
+.fa-exclamation-circle::before {
+  content: "\f06a"; }
+
+.fa-school-circle-xmark::before {
+  content: "\e56d"; }
+
+.fa-arrow-right-from-bracket::before {
+  content: "\f08b"; }
+
+.fa-sign-out::before {
+  content: "\f08b"; }
+
+.fa-circle-chevron-down::before {
+  content: "\f13a"; }
+
+.fa-chevron-circle-down::before {
+  content: "\f13a"; }
+
+.fa-unlock-keyhole::before {
+  content: "\f13e"; }
+
+.fa-unlock-alt::before {
+  content: "\f13e"; }
+
+.fa-cloud-showers-heavy::before {
+  content: "\f740"; }
+
+.fa-headphones-simple::before {
+  content: "\f58f"; }
+
+.fa-headphones-alt::before {
+  content: "\f58f"; }
+
+.fa-sitemap::before {
+  content: "\f0e8"; }
+
+.fa-circle-dollar-to-slot::before {
+  content: "\f4b9"; }
+
+.fa-donate::before {
+  content: "\f4b9"; }
+
+.fa-memory::before {
+  content: "\f538"; }
+
+.fa-road-spikes::before {
+  content: "\e568"; }
+
+.fa-fire-burner::before {
+  content: "\e4f1"; }
+
+.fa-flag::before {
+  content: "\f024"; }
+
+.fa-hanukiah::before {
+  content: "\f6e6"; }
+
+.fa-feather::before {
+  content: "\f52d"; }
+
+.fa-volume-low::before {
+  content: "\f027"; }
+
+.fa-volume-down::before {
+  content: "\f027"; }
+
+.fa-comment-slash::before {
+  content: "\f4b3"; }
+
+.fa-cloud-sun-rain::before {
+  content: "\f743"; }
+
+.fa-compress::before {
+  content: "\f066"; }
+
+.fa-wheat-awn::before {
+  content: "\e2cd"; }
+
+.fa-wheat-alt::before {
+  content: "\e2cd"; }
+
+.fa-ankh::before {
+  content: "\f644"; }
+
+.fa-hands-holding-child::before {
+  content: "\e4fa"; }
+
+.fa-asterisk::before {
+  content: "\2a"; }
+
+.fa-square-check::before {
+  content: "\f14a"; }
+
+.fa-check-square::before {
+  content: "\f14a"; }
+
+.fa-peseta-sign::before {
+  content: "\e221"; }
+
+.fa-heading::before {
+  content: "\f1dc"; }
+
+.fa-header::before {
+  content: "\f1dc"; }
+
+.fa-ghost::before {
+  content: "\f6e2"; }
+
+.fa-list::before {
+  content: "\f03a"; }
+
+.fa-list-squares::before {
+  content: "\f03a"; }
+
+.fa-square-phone-flip::before {
+  content: "\f87b"; }
+
+.fa-phone-square-alt::before {
+  content: "\f87b"; }
+
+.fa-cart-plus::before {
+  content: "\f217"; }
+
+.fa-gamepad::before {
+  content: "\f11b"; }
+
+.fa-circle-dot::before {
+  content: "\f192"; }
+
+.fa-dot-circle::before {
+  content: "\f192"; }
+
+.fa-face-dizzy::before {
+  content: "\f567"; }
+
+.fa-dizzy::before {
+  content: "\f567"; }
+
+.fa-egg::before {
+  content: "\f7fb"; }
+
+.fa-house-medical-circle-xmark::before {
+  content: "\e513"; }
+
+.fa-campground::before {
+  content: "\f6bb"; }
+
+.fa-folder-plus::before {
+  content: "\f65e"; }
+
+.fa-futbol::before {
+  content: "\f1e3"; }
+
+.fa-futbol-ball::before {
+  content: "\f1e3"; }
+
+.fa-soccer-ball::before {
+  content: "\f1e3"; }
+
+.fa-paintbrush::before {
+  content: "\f1fc"; }
+
+.fa-paint-brush::before {
+  content: "\f1fc"; }
+
+.fa-lock::before {
+  content: "\f023"; }
+
+.fa-gas-pump::before {
+  content: "\f52f"; }
+
+.fa-hot-tub-person::before {
+  content: "\f593"; }
+
+.fa-hot-tub::before {
+  content: "\f593"; }
+
+.fa-map-location::before {
+  content: "\f59f"; }
+
+.fa-map-marked::before {
+  content: "\f59f"; }
+
+.fa-house-flood-water::before {
+  content: "\e50e"; }
+
+.fa-tree::before {
+  content: "\f1bb"; }
+
+.fa-bridge-lock::before {
+  content: "\e4cc"; }
+
+.fa-sack-dollar::before {
+  content: "\f81d"; }
+
+.fa-pen-to-square::before {
+  content: "\f044"; }
+
+.fa-edit::before {
+  content: "\f044"; }
+
+.fa-car-side::before {
+  content: "\f5e4"; }
+
+.fa-share-nodes::before {
+  content: "\f1e0"; }
+
+.fa-share-alt::before {
+  content: "\f1e0"; }
+
+.fa-heart-circle-minus::before {
+  content: "\e4ff"; }
+
+.fa-hourglass-half::before {
+  content: "\f252"; }
+
+.fa-hourglass-2::before {
+  content: "\f252"; }
+
+.fa-microscope::before {
+  content: "\f610"; }
+
+.fa-sink::before {
+  content: "\e06d"; }
+
+.fa-bag-shopping::before {
+  content: "\f290"; }
+
+.fa-shopping-bag::before {
+  content: "\f290"; }
+
+.fa-arrow-down-z-a::before {
+  content: "\f881"; }
+
+.fa-sort-alpha-desc::before {
+  content: "\f881"; }
+
+.fa-sort-alpha-down-alt::before {
+  content: "\f881"; }
+
+.fa-mitten::before {
+  content: "\f7b5"; }
+
+.fa-person-rays::before {
+  content: "\e54d"; }
+
+.fa-users::before {
+  content: "\f0c0"; }
+
+.fa-eye-slash::before {
+  content: "\f070"; }
+
+.fa-flask-vial::before {
+  content: "\e4f3"; }
+
+.fa-hand::before {
+  content: "\f256"; }
+
+.fa-hand-paper::before {
+  content: "\f256"; }
+
+.fa-om::before {
+  content: "\f679"; }
+
+.fa-worm::before {
+  content: "\e599"; }
+
+.fa-house-circle-xmark::before {
+  content: "\e50b"; }
+
+.fa-plug::before {
+  content: "\f1e6"; }
+
+.fa-chevron-up::before {
+  content: "\f077"; }
+
+.fa-hand-spock::before {
+  content: "\f259"; }
+
+.fa-stopwatch::before {
+  content: "\f2f2"; }
+
+.fa-face-kiss::before {
+  content: "\f596"; }
+
+.fa-kiss::before {
+  content: "\f596"; }
+
+.fa-bridge-circle-xmark::before {
+  content: "\e4cb"; }
+
+.fa-face-grin-tongue::before {
+  content: "\f589"; }
+
+.fa-grin-tongue::before {
+  content: "\f589"; }
+
+.fa-chess-bishop::before {
+  content: "\f43a"; }
+
+.fa-face-grin-wink::before {
+  content: "\f58c"; }
+
+.fa-grin-wink::before {
+  content: "\f58c"; }
+
+.fa-ear-deaf::before {
+  content: "\f2a4"; }
+
+.fa-deaf::before {
+  content: "\f2a4"; }
+
+.fa-deafness::before {
+  content: "\f2a4"; }
+
+.fa-hard-of-hearing::before {
+  content: "\f2a4"; }
+
+.fa-road-circle-check::before {
+  content: "\e564"; }
+
+.fa-dice-five::before {
+  content: "\f523"; }
+
+.fa-square-rss::before {
+  content: "\f143"; }
+
+.fa-rss-square::before {
+  content: "\f143"; }
+
+.fa-land-mine-on::before {
+  content: "\e51b"; }
+
+.fa-i-cursor::before {
+  content: "\f246"; }
+
+.fa-stamp::before {
+  content: "\f5bf"; }
+
+.fa-stairs::before {
+  content: "\e289"; }
+
+.fa-i::before {
+  content: "\49"; }
+
+.fa-hryvnia-sign::before {
+  content: "\f6f2"; }
+
+.fa-hryvnia::before {
+  content: "\f6f2"; }
+
+.fa-pills::before {
+  content: "\f484"; }
+
+.fa-face-grin-wide::before {
+  content: "\f581"; }
+
+.fa-grin-alt::before {
+  content: "\f581"; }
+
+.fa-tooth::before {
+  content: "\f5c9"; }
+
+.fa-v::before {
+  content: "\56"; }
+
+.fa-bangladeshi-taka-sign::before {
+  content: "\e2e6"; }
+
+.fa-bicycle::before {
+  content: "\f206"; }
+
+.fa-staff-snake::before {
+  content: "\e579"; }
+
+.fa-rod-asclepius::before {
+  content: "\e579"; }
+
+.fa-rod-snake::before {
+  content: "\e579"; }
+
+.fa-staff-aesculapius::before {
+  content: "\e579"; }
+
+.fa-head-side-cough-slash::before {
+  content: "\e062"; }
+
+.fa-truck-medical::before {
+  content: "\f0f9"; }
+
+.fa-ambulance::before {
+  content: "\f0f9"; }
+
+.fa-wheat-awn-circle-exclamation::before {
+  content: "\e598"; }
+
+.fa-snowman::before {
+  content: "\f7d0"; }
+
+.fa-mortar-pestle::before {
+  content: "\f5a7"; }
+
+.fa-road-barrier::before {
+  content: "\e562"; }
+
+.fa-school::before {
+  content: "\f549"; }
+
+.fa-igloo::before {
+  content: "\f7ae"; }
+
+.fa-joint::before {
+  content: "\f595"; }
+
+.fa-angle-right::before {
+  content: "\f105"; }
+
+.fa-horse::before {
+  content: "\f6f0"; }
+
+.fa-q::before {
+  content: "\51"; }
+
+.fa-g::before {
+  content: "\47"; }
+
+.fa-notes-medical::before {
+  content: "\f481"; }
+
+.fa-temperature-half::before {
+  content: "\f2c9"; }
+
+.fa-temperature-2::before {
+  content: "\f2c9"; }
+
+.fa-thermometer-2::before {
+  content: "\f2c9"; }
+
+.fa-thermometer-half::before {
+  content: "\f2c9"; }
+
+.fa-dong-sign::before {
+  content: "\e169"; }
+
+.fa-capsules::before {
+  content: "\f46b"; }
+
+.fa-poo-storm::before {
+  content: "\f75a"; }
+
+.fa-poo-bolt::before {
+  content: "\f75a"; }
+
+.fa-face-frown-open::before {
+  content: "\f57a"; }
+
+.fa-frown-open::before {
+  content: "\f57a"; }
+
+.fa-hand-point-up::before {
+  content: "\f0a6"; }
+
+.fa-money-bill::before {
+  content: "\f0d6"; }
+
+.fa-bookmark::before {
+  content: "\f02e"; }
+
+.fa-align-justify::before {
+  content: "\f039"; }
+
+.fa-umbrella-beach::before {
+  content: "\f5ca"; }
+
+.fa-helmet-un::before {
+  content: "\e503"; }
+
+.fa-bullseye::before {
+  content: "\f140"; }
+
+.fa-bacon::before {
+  content: "\f7e5"; }
+
+.fa-hand-point-down::before {
+  content: "\f0a7"; }
+
+.fa-arrow-up-from-bracket::before {
+  content: "\e09a"; }
+
+.fa-folder::before {
+  content: "\f07b"; }
+
+.fa-folder-blank::before {
+  content: "\f07b"; }
+
+.fa-file-waveform::before {
+  content: "\f478"; }
+
+.fa-file-medical-alt::before {
+  content: "\f478"; }
+
+.fa-radiation::before {
+  content: "\f7b9"; }
+
+.fa-chart-simple::before {
+  content: "\e473"; }
+
+.fa-mars-stroke::before {
+  content: "\f229"; }
+
+.fa-vial::before {
+  content: "\f492"; }
+
+.fa-gauge::before {
+  content: "\f624"; }
+
+.fa-dashboard::before {
+  content: "\f624"; }
+
+.fa-gauge-med::before {
+  content: "\f624"; }
+
+.fa-tachometer-alt-average::before {
+  content: "\f624"; }
+
+.fa-wand-magic-sparkles::before {
+  content: "\e2ca"; }
+
+.fa-magic-wand-sparkles::before {
+  content: "\e2ca"; }
+
+.fa-e::before {
+  content: "\45"; }
+
+.fa-pen-clip::before {
+  content: "\f305"; }
+
+.fa-pen-alt::before {
+  content: "\f305"; }
+
+.fa-bridge-circle-exclamation::before {
+  content: "\e4ca"; }
+
+.fa-user::before {
+  content: "\f007"; }
+
+.fa-school-circle-check::before {
+  content: "\e56b"; }
+
+.fa-dumpster::before {
+  content: "\f793"; }
+
+.fa-van-shuttle::before {
+  content: "\f5b6"; }
+
+.fa-shuttle-van::before {
+  content: "\f5b6"; }
+
+.fa-building-user::before {
+  content: "\e4da"; }
+
+.fa-square-caret-left::before {
+  content: "\f191"; }
+
+.fa-caret-square-left::before {
+  content: "\f191"; }
+
+.fa-highlighter::before {
+  content: "\f591"; }
+
+.fa-key::before {
+  content: "\f084"; }
+
+.fa-bullhorn::before {
+  content: "\f0a1"; }
+
+.fa-globe::before {
+  content: "\f0ac"; }
+
+.fa-synagogue::before {
+  content: "\f69b"; }
+
+.fa-person-half-dress::before {
+  content: "\e548"; }
+
+.fa-road-bridge::before {
+  content: "\e563"; }
+
+.fa-location-arrow::before {
+  content: "\f124"; }
+
+.fa-c::before {
+  content: "\43"; }
+
+.fa-tablet-button::before {
+  content: "\f10a"; }
+
+.fa-building-lock::before {
+  content: "\e4d6"; }
+
+.fa-pizza-slice::before {
+  content: "\f818"; }
+
+.fa-money-bill-wave::before {
+  content: "\f53a"; }
+
+.fa-chart-area::before {
+  content: "\f1fe"; }
+
+.fa-area-chart::before {
+  content: "\f1fe"; }
+
+.fa-house-flag::before {
+  content: "\e50d"; }
+
+.fa-person-circle-minus::before {
+  content: "\e540"; }
+
+.fa-ban::before {
+  content: "\f05e"; }
+
+.fa-cancel::before {
+  content: "\f05e"; }
+
+.fa-camera-rotate::before {
+  content: "\e0d8"; }
+
+.fa-spray-can-sparkles::before {
+  content: "\f5d0"; }
+
+.fa-air-freshener::before {
+  content: "\f5d0"; }
+
+.fa-star::before {
+  content: "\f005"; }
+
+.fa-repeat::before {
+  content: "\f363"; }
+
+.fa-cross::before {
+  content: "\f654"; }
+
+.fa-box::before {
+  content: "\f466"; }
+
+.fa-venus-mars::before {
+  content: "\f228"; }
+
+.fa-arrow-pointer::before {
+  content: "\f245"; }
+
+.fa-mouse-pointer::before {
+  content: "\f245"; }
+
+.fa-maximize::before {
+  content: "\f31e"; }
+
+.fa-expand-arrows-alt::before {
+  content: "\f31e"; }
+
+.fa-charging-station::before {
+  content: "\f5e7"; }
+
+.fa-shapes::before {
+  content: "\f61f"; }
+
+.fa-triangle-circle-square::before {
+  content: "\f61f"; }
+
+.fa-shuffle::before {
+  content: "\f074"; }
+
+.fa-random::before {
+  content: "\f074"; }
+
+.fa-person-running::before {
+  content: "\f70c"; }
+
+.fa-running::before {
+  content: "\f70c"; }
+
+.fa-mobile-retro::before {
+  content: "\e527"; }
+
+.fa-grip-lines-vertical::before {
+  content: "\f7a5"; }
+
+.fa-spider::before {
+  content: "\f717"; }
+
+.fa-hands-bound::before {
+  content: "\e4f9"; }
+
+.fa-file-invoice-dollar::before {
+  content: "\f571"; }
+
+.fa-plane-circle-exclamation::before {
+  content: "\e556"; }
+
+.fa-x-ray::before {
+  content: "\f497"; }
+
+.fa-spell-check::before {
+  content: "\f891"; }
+
+.fa-slash::before {
+  content: "\f715"; }
+
+.fa-computer-mouse::before {
+  content: "\f8cc"; }
+
+.fa-mouse::before {
+  content: "\f8cc"; }
+
+.fa-arrow-right-to-bracket::before {
+  content: "\f090"; }
+
+.fa-sign-in::before {
+  content: "\f090"; }
+
+.fa-shop-slash::before {
+  content: "\e070"; }
+
+.fa-store-alt-slash::before {
+  content: "\e070"; }
+
+.fa-server::before {
+  content: "\f233"; }
+
+.fa-virus-covid-slash::before {
+  content: "\e4a9"; }
+
+.fa-shop-lock::before {
+  content: "\e4a5"; }
+
+.fa-hourglass-start::before {
+  content: "\f251"; }
+
+.fa-hourglass-1::before {
+  content: "\f251"; }
+
+.fa-blender-phone::before {
+  content: "\f6b6"; }
+
+.fa-building-wheat::before {
+  content: "\e4db"; }
+
+.fa-person-breastfeeding::before {
+  content: "\e53a"; }
+
+.fa-right-to-bracket::before {
+  content: "\f2f6"; }
+
+.fa-sign-in-alt::before {
+  content: "\f2f6"; }
+
+.fa-venus::before {
+  content: "\f221"; }
+
+.fa-passport::before {
+  content: "\f5ab"; }
+
+.fa-heart-pulse::before {
+  content: "\f21e"; }
+
+.fa-heartbeat::before {
+  content: "\f21e"; }
+
+.fa-people-carry-box::before {
+  content: "\f4ce"; }
+
+.fa-people-carry::before {
+  content: "\f4ce"; }
+
+.fa-temperature-high::before {
+  content: "\f769"; }
+
+.fa-microchip::before {
+  content: "\f2db"; }
+
+.fa-crown::before {
+  content: "\f521"; }
+
+.fa-weight-hanging::before {
+  content: "\f5cd"; }
+
+.fa-xmarks-lines::before {
+  content: "\e59a"; }
+
+.fa-file-prescription::before {
+  content: "\f572"; }
+
+.fa-weight-scale::before {
+  content: "\f496"; }
+
+.fa-weight::before {
+  content: "\f496"; }
+
+.fa-user-group::before {
+  content: "\f500"; }
+
+.fa-user-friends::before {
+  content: "\f500"; }
+
+.fa-arrow-up-a-z::before {
+  content: "\f15e"; }
+
+.fa-sort-alpha-up::before {
+  content: "\f15e"; }
+
+.fa-chess-knight::before {
+  content: "\f441"; }
+
+.fa-face-laugh-squint::before {
+  content: "\f59b"; }
+
+.fa-laugh-squint::before {
+  content: "\f59b"; }
+
+.fa-wheelchair::before {
+  content: "\f193"; }
+
+.fa-circle-arrow-up::before {
+  content: "\f0aa"; }
+
+.fa-arrow-circle-up::before {
+  content: "\f0aa"; }
+
+.fa-toggle-on::before {
+  content: "\f205"; }
+
+.fa-person-walking::before {
+  content: "\f554"; }
+
+.fa-walking::before {
+  content: "\f554"; }
+
+.fa-l::before {
+  content: "\4c"; }
+
+.fa-fire::before {
+  content: "\f06d"; }
+
+.fa-bed-pulse::before {
+  content: "\f487"; }
+
+.fa-procedures::before {
+  content: "\f487"; }
+
+.fa-shuttle-space::before {
+  content: "\f197"; }
+
+.fa-space-shuttle::before {
+  content: "\f197"; }
+
+.fa-face-laugh::before {
+  content: "\f599"; }
+
+.fa-laugh::before {
+  content: "\f599"; }
+
+.fa-folder-open::before {
+  content: "\f07c"; }
+
+.fa-heart-circle-plus::before {
+  content: "\e500"; }
+
+.fa-code-fork::before {
+  content: "\e13b"; }
+
+.fa-city::before {
+  content: "\f64f"; }
+
+.fa-microphone-lines::before {
+  content: "\f3c9"; }
+
+.fa-microphone-alt::before {
+  content: "\f3c9"; }
+
+.fa-pepper-hot::before {
+  content: "\f816"; }
+
+.fa-unlock::before {
+  content: "\f09c"; }
+
+.fa-colon-sign::before {
+  content: "\e140"; }
+
+.fa-headset::before {
+  content: "\f590"; }
+
+.fa-store-slash::before {
+  content: "\e071"; }
+
+.fa-road-circle-xmark::before {
+  content: "\e566"; }
+
+.fa-user-minus::before {
+  content: "\f503"; }
+
+.fa-mars-stroke-up::before {
+  content: "\f22a"; }
+
+.fa-mars-stroke-v::before {
+  content: "\f22a"; }
+
+.fa-champagne-glasses::before {
+  content: "\f79f"; }
+
+.fa-glass-cheers::before {
+  content: "\f79f"; }
+
+.fa-clipboard::before {
+  content: "\f328"; }
+
+.fa-house-circle-exclamation::before {
+  content: "\e50a"; }
+
+.fa-file-arrow-up::before {
+  content: "\f574"; }
+
+.fa-file-upload::before {
+  content: "\f574"; }
+
+.fa-wifi::before {
+  content: "\f1eb"; }
+
+.fa-wifi-3::before {
+  content: "\f1eb"; }
+
+.fa-wifi-strong::before {
+  content: "\f1eb"; }
+
+.fa-bath::before {
+  content: "\f2cd"; }
+
+.fa-bathtub::before {
+  content: "\f2cd"; }
+
+.fa-underline::before {
+  content: "\f0cd"; }
+
+.fa-user-pen::before {
+  content: "\f4ff"; }
+
+.fa-user-edit::before {
+  content: "\f4ff"; }
+
+.fa-signature::before {
+  content: "\f5b7"; }
+
+.fa-stroopwafel::before {
+  content: "\f551"; }
+
+.fa-bold::before {
+  content: "\f032"; }
+
+.fa-anchor-lock::before {
+  content: "\e4ad"; }
+
+.fa-building-ngo::before {
+  content: "\e4d7"; }
+
+.fa-manat-sign::before {
+  content: "\e1d5"; }
+
+.fa-not-equal::before {
+  content: "\f53e"; }
+
+.fa-border-top-left::before {
+  content: "\f853"; }
+
+.fa-border-style::before {
+  content: "\f853"; }
+
+.fa-map-location-dot::before {
+  content: "\f5a0"; }
+
+.fa-map-marked-alt::before {
+  content: "\f5a0"; }
+
+.fa-jedi::before {
+  content: "\f669"; }
+
+.fa-square-poll-vertical::before {
+  content: "\f681"; }
+
+.fa-poll::before {
+  content: "\f681"; }
+
+.fa-mug-hot::before {
+  content: "\f7b6"; }
+
+.fa-car-battery::before {
+  content: "\f5df"; }
+
+.fa-battery-car::before {
+  content: "\f5df"; }
+
+.fa-gift::before {
+  content: "\f06b"; }
+
+.fa-dice-two::before {
+  content: "\f528"; }
+
+.fa-chess-queen::before {
+  content: "\f445"; }
+
+.fa-glasses::before {
+  content: "\f530"; }
+
+.fa-chess-board::before {
+  content: "\f43c"; }
+
+.fa-building-circle-check::before {
+  content: "\e4d2"; }
+
+.fa-person-chalkboard::before {
+  content: "\e53d"; }
+
+.fa-mars-stroke-right::before {
+  content: "\f22b"; }
+
+.fa-mars-stroke-h::before {
+  content: "\f22b"; }
+
+.fa-hand-back-fist::before {
+  content: "\f255"; }
+
+.fa-hand-rock::before {
+  content: "\f255"; }
+
+.fa-square-caret-up::before {
+  content: "\f151"; }
+
+.fa-caret-square-up::before {
+  content: "\f151"; }
+
+.fa-cloud-showers-water::before {
+  content: "\e4e4"; }
+
+.fa-chart-bar::before {
+  content: "\f080"; }
+
+.fa-bar-chart::before {
+  content: "\f080"; }
+
+.fa-hands-bubbles::before {
+  content: "\e05e"; }
+
+.fa-hands-wash::before {
+  content: "\e05e"; }
+
+.fa-less-than-equal::before {
+  content: "\f537"; }
+
+.fa-train::before {
+  content: "\f238"; }
+
+.fa-eye-low-vision::before {
+  content: "\f2a8"; }
+
+.fa-low-vision::before {
+  content: "\f2a8"; }
+
+.fa-crow::before {
+  content: "\f520"; }
+
+.fa-sailboat::before {
+  content: "\e445"; }
+
+.fa-window-restore::before {
+  content: "\f2d2"; }
+
+.fa-square-plus::before {
+  content: "\f0fe"; }
+
+.fa-plus-square::before {
+  content: "\f0fe"; }
+
+.fa-torii-gate::before {
+  content: "\f6a1"; }
+
+.fa-frog::before {
+  content: "\f52e"; }
+
+.fa-bucket::before {
+  content: "\e4cf"; }
+
+.fa-image::before {
+  content: "\f03e"; }
+
+.fa-microphone::before {
+  content: "\f130"; }
+
+.fa-cow::before {
+  content: "\f6c8"; }
+
+.fa-caret-up::before {
+  content: "\f0d8"; }
+
+.fa-screwdriver::before {
+  content: "\f54a"; }
+
+.fa-folder-closed::before {
+  content: "\e185"; }
+
+.fa-house-tsunami::before {
+  content: "\e515"; }
+
+.fa-square-nfi::before {
+  content: "\e576"; }
+
+.fa-arrow-up-from-ground-water::before {
+  content: "\e4b5"; }
+
+.fa-martini-glass::before {
+  content: "\f57b"; }
+
+.fa-glass-martini-alt::before {
+  content: "\f57b"; }
+
+.fa-rotate-left::before {
+  content: "\f2ea"; }
+
+.fa-rotate-back::before {
+  content: "\f2ea"; }
+
+.fa-rotate-backward::before {
+  content: "\f2ea"; }
+
+.fa-undo-alt::before {
+  content: "\f2ea"; }
+
+.fa-table-columns::before {
+  content: "\f0db"; }
+
+.fa-columns::before {
+  content: "\f0db"; }
+
+.fa-lemon::before {
+  content: "\f094"; }
+
+.fa-head-side-mask::before {
+  content: "\e063"; }
+
+.fa-handshake::before {
+  content: "\f2b5"; }
+
+.fa-gem::before {
+  content: "\f3a5"; }
+
+.fa-dolly::before {
+  content: "\f472"; }
+
+.fa-dolly-box::before {
+  content: "\f472"; }
+
+.fa-smoking::before {
+  content: "\f48d"; }
+
+.fa-minimize::before {
+  content: "\f78c"; }
+
+.fa-compress-arrows-alt::before {
+  content: "\f78c"; }
+
+.fa-monument::before {
+  content: "\f5a6"; }
+
+.fa-snowplow::before {
+  content: "\f7d2"; }
+
+.fa-angles-right::before {
+  content: "\f101"; }
+
+.fa-angle-double-right::before {
+  content: "\f101"; }
+
+.fa-cannabis::before {
+  content: "\f55f"; }
+
+.fa-circle-play::before {
+  content: "\f144"; }
+
+.fa-play-circle::before {
+  content: "\f144"; }
+
+.fa-tablets::before {
+  content: "\f490"; }
+
+.fa-ethernet::before {
+  content: "\f796"; }
+
+.fa-euro-sign::before {
+  content: "\f153"; }
+
+.fa-eur::before {
+  content: "\f153"; }
+
+.fa-euro::before {
+  content: "\f153"; }
+
+.fa-chair::before {
+  content: "\f6c0"; }
+
+.fa-circle-check::before {
+  content: "\f058"; }
+
+.fa-check-circle::before {
+  content: "\f058"; }
+
+.fa-circle-stop::before {
+  content: "\f28d"; }
+
+.fa-stop-circle::before {
+  content: "\f28d"; }
+
+.fa-compass-drafting::before {
+  content: "\f568"; }
+
+.fa-drafting-compass::before {
+  content: "\f568"; }
+
+.fa-plate-wheat::before {
+  content: "\e55a"; }
+
+.fa-icicles::before {
+  content: "\f7ad"; }
+
+.fa-person-shelter::before {
+  content: "\e54f"; }
+
+.fa-neuter::before {
+  content: "\f22c"; }
+
+.fa-id-badge::before {
+  content: "\f2c1"; }
+
+.fa-marker::before {
+  content: "\f5a1"; }
+
+.fa-face-laugh-beam::before {
+  content: "\f59a"; }
+
+.fa-laugh-beam::before {
+  content: "\f59a"; }
+
+.fa-helicopter-symbol::before {
+  content: "\e502"; }
+
+.fa-universal-access::before {
+  content: "\f29a"; }
+
+.fa-circle-chevron-up::before {
+  content: "\f139"; }
+
+.fa-chevron-circle-up::before {
+  content: "\f139"; }
+
+.fa-lari-sign::before {
+  content: "\e1c8"; }
+
+.fa-volcano::before {
+  content: "\f770"; }
+
+.fa-person-walking-dashed-line-arrow-right::before {
+  content: "\e553"; }
+
+.fa-sterling-sign::before {
+  content: "\f154"; }
+
+.fa-gbp::before {
+  content: "\f154"; }
+
+.fa-pound-sign::before {
+  content: "\f154"; }
+
+.fa-viruses::before {
+  content: "\e076"; }
+
+.fa-square-person-confined::before {
+  content: "\e577"; }
+
+.fa-user-tie::before {
+  content: "\f508"; }
+
+.fa-arrow-down-long::before {
+  content: "\f175"; }
+
+.fa-long-arrow-down::before {
+  content: "\f175"; }
+
+.fa-tent-arrow-down-to-line::before {
+  content: "\e57e"; }
+
+.fa-certificate::before {
+  content: "\f0a3"; }
+
+.fa-reply-all::before {
+  content: "\f122"; }
+
+.fa-mail-reply-all::before {
+  content: "\f122"; }
+
+.fa-suitcase::before {
+  content: "\f0f2"; }
+
+.fa-person-skating::before {
+  content: "\f7c5"; }
+
+.fa-skating::before {
+  content: "\f7c5"; }
+
+.fa-filter-circle-dollar::before {
+  content: "\f662"; }
+
+.fa-funnel-dollar::before {
+  content: "\f662"; }
+
+.fa-camera-retro::before {
+  content: "\f083"; }
+
+.fa-circle-arrow-down::before {
+  content: "\f0ab"; }
+
+.fa-arrow-circle-down::before {
+  content: "\f0ab"; }
+
+.fa-file-import::before {
+  content: "\f56f"; }
+
+.fa-arrow-right-to-file::before {
+  content: "\f56f"; }
+
+.fa-square-arrow-up-right::before {
+  content: "\f14c"; }
+
+.fa-external-link-square::before {
+  content: "\f14c"; }
+
+.fa-box-open::before {
+  content: "\f49e"; }
+
+.fa-scroll::before {
+  content: "\f70e"; }
+
+.fa-spa::before {
+  content: "\f5bb"; }
+
+.fa-location-pin-lock::before {
+  content: "\e51f"; }
+
+.fa-pause::before {
+  content: "\f04c"; }
+
+.fa-hill-avalanche::before {
+  content: "\e507"; }
+
+.fa-temperature-empty::before {
+  content: "\f2cb"; }
+
+.fa-temperature-0::before {
+  content: "\f2cb"; }
+
+.fa-thermometer-0::before {
+  content: "\f2cb"; }
+
+.fa-thermometer-empty::before {
+  content: "\f2cb"; }
+
+.fa-bomb::before {
+  content: "\f1e2"; }
+
+.fa-registered::before {
+  content: "\f25d"; }
+
+.fa-address-card::before {
+  content: "\f2bb"; }
+
+.fa-contact-card::before {
+  content: "\f2bb"; }
+
+.fa-vcard::before {
+  content: "\f2bb"; }
+
+.fa-scale-unbalanced-flip::before {
+  content: "\f516"; }
+
+.fa-balance-scale-right::before {
+  content: "\f516"; }
+
+.fa-subscript::before {
+  content: "\f12c"; }
+
+.fa-diamond-turn-right::before {
+  content: "\f5eb"; }
+
+.fa-directions::before {
+  content: "\f5eb"; }
+
+.fa-burst::before {
+  content: "\e4dc"; }
+
+.fa-house-laptop::before {
+  content: "\e066"; }
+
+.fa-laptop-house::before {
+  content: "\e066"; }
+
+.fa-face-tired::before {
+  content: "\f5c8"; }
+
+.fa-tired::before {
+  content: "\f5c8"; }
+
+.fa-money-bills::before {
+  content: "\e1f3"; }
+
+.fa-smog::before {
+  content: "\f75f"; }
+
+.fa-crutch::before {
+  content: "\f7f7"; }
+
+.fa-cloud-arrow-up::before {
+  content: "\f0ee"; }
+
+.fa-cloud-upload::before {
+  content: "\f0ee"; }
+
+.fa-cloud-upload-alt::before {
+  content: "\f0ee"; }
+
+.fa-palette::before {
+  content: "\f53f"; }
+
+.fa-arrows-turn-right::before {
+  content: "\e4c0"; }
+
+.fa-vest::before {
+  content: "\e085"; }
+
+.fa-ferry::before {
+  content: "\e4ea"; }
+
+.fa-arrows-down-to-people::before {
+  content: "\e4b9"; }
+
+.fa-seedling::before {
+  content: "\f4d8"; }
+
+.fa-sprout::before {
+  content: "\f4d8"; }
+
+.fa-left-right::before {
+  content: "\f337"; }
+
+.fa-arrows-alt-h::before {
+  content: "\f337"; }
+
+.fa-boxes-packing::before {
+  content: "\e4c7"; }
+
+.fa-circle-arrow-left::before {
+  content: "\f0a8"; }
+
+.fa-arrow-circle-left::before {
+  content: "\f0a8"; }
+
+.fa-group-arrows-rotate::before {
+  content: "\e4f6"; }
+
+.fa-bowl-food::before {
+  content: "\e4c6"; }
+
+.fa-candy-cane::before {
+  content: "\f786"; }
+
+.fa-arrow-down-wide-short::before {
+  content: "\f160"; }
+
+.fa-sort-amount-asc::before {
+  content: "\f160"; }
+
+.fa-sort-amount-down::before {
+  content: "\f160"; }
+
+.fa-cloud-bolt::before {
+  content: "\f76c"; }
+
+.fa-thunderstorm::before {
+  content: "\f76c"; }
+
+.fa-text-slash::before {
+  content: "\f87d"; }
+
+.fa-remove-format::before {
+  content: "\f87d"; }
+
+.fa-face-smile-wink::before {
+  content: "\f4da"; }
+
+.fa-smile-wink::before {
+  content: "\f4da"; }
+
+.fa-file-word::before {
+  content: "\f1c2"; }
+
+.fa-file-powerpoint::before {
+  content: "\f1c4"; }
+
+.fa-arrows-left-right::before {
+  content: "\f07e"; }
+
+.fa-arrows-h::before {
+  content: "\f07e"; }
+
+.fa-house-lock::before {
+  content: "\e510"; }
+
+.fa-cloud-arrow-down::before {
+  content: "\f0ed"; }
+
+.fa-cloud-download::before {
+  content: "\f0ed"; }
+
+.fa-cloud-download-alt::before {
+  content: "\f0ed"; }
+
+.fa-children::before {
+  content: "\e4e1"; }
+
+.fa-chalkboard::before {
+  content: "\f51b"; }
+
+.fa-blackboard::before {
+  content: "\f51b"; }
+
+.fa-user-large-slash::before {
+  content: "\f4fa"; }
+
+.fa-user-alt-slash::before {
+  content: "\f4fa"; }
+
+.fa-envelope-open::before {
+  content: "\f2b6"; }
+
+.fa-handshake-simple-slash::before {
+  content: "\e05f"; }
+
+.fa-handshake-alt-slash::before {
+  content: "\e05f"; }
+
+.fa-mattress-pillow::before {
+  content: "\e525"; }
+
+.fa-guarani-sign::before {
+  content: "\e19a"; }
+
+.fa-arrows-rotate::before {
+  content: "\f021"; }
+
+.fa-refresh::before {
+  content: "\f021"; }
+
+.fa-sync::before {
+  content: "\f021"; }
+
+.fa-fire-extinguisher::before {
+  content: "\f134"; }
+
+.fa-cruzeiro-sign::before {
+  content: "\e152"; }
+
+.fa-greater-than-equal::before {
+  content: "\f532"; }
+
+.fa-shield-halved::before {
+  content: "\f3ed"; }
+
+.fa-shield-alt::before {
+  content: "\f3ed"; }
+
+.fa-book-atlas::before {
+  content: "\f558"; }
+
+.fa-atlas::before {
+  content: "\f558"; }
+
+.fa-virus::before {
+  content: "\e074"; }
+
+.fa-envelope-circle-check::before {
+  content: "\e4e8"; }
+
+.fa-layer-group::before {
+  content: "\f5fd"; }
+
+.fa-arrows-to-dot::before {
+  content: "\e4be"; }
+
+.fa-archway::before {
+  content: "\f557"; }
+
+.fa-heart-circle-check::before {
+  content: "\e4fd"; }
+
+.fa-house-chimney-crack::before {
+  content: "\f6f1"; }
+
+.fa-house-damage::before {
+  content: "\f6f1"; }
+
+.fa-file-zipper::before {
+  content: "\f1c6"; }
+
+.fa-file-archive::before {
+  content: "\f1c6"; }
+
+.fa-square::before {
+  content: "\f0c8"; }
+
+.fa-martini-glass-empty::before {
+  content: "\f000"; }
+
+.fa-glass-martini::before {
+  content: "\f000"; }
+
+.fa-couch::before {
+  content: "\f4b8"; }
+
+.fa-cedi-sign::before {
+  content: "\e0df"; }
+
+.fa-italic::before {
+  content: "\f033"; }
+
+.fa-church::before {
+  content: "\f51d"; }
+
+.fa-comments-dollar::before {
+  content: "\f653"; }
+
+.fa-democrat::before {
+  content: "\f747"; }
+
+.fa-z::before {
+  content: "\5a"; }
+
+.fa-person-skiing::before {
+  content: "\f7c9"; }
+
+.fa-skiing::before {
+  content: "\f7c9"; }
+
+.fa-road-lock::before {
+  content: "\e567"; }
+
+.fa-a::before {
+  content: "\41"; }
+
+.fa-temperature-arrow-down::before {
+  content: "\e03f"; }
+
+.fa-temperature-down::before {
+  content: "\e03f"; }
+
+.fa-feather-pointed::before {
+  content: "\f56b"; }
+
+.fa-feather-alt::before {
+  content: "\f56b"; }
+
+.fa-p::before {
+  content: "\50"; }
+
+.fa-snowflake::before {
+  content: "\f2dc"; }
+
+.fa-newspaper::before {
+  content: "\f1ea"; }
+
+.fa-rectangle-ad::before {
+  content: "\f641"; }
+
+.fa-ad::before {
+  content: "\f641"; }
+
+.fa-circle-arrow-right::before {
+  content: "\f0a9"; }
+
+.fa-arrow-circle-right::before {
+  content: "\f0a9"; }
+
+.fa-filter-circle-xmark::before {
+  content: "\e17b"; }
+
+.fa-locust::before {
+  content: "\e520"; }
+
+.fa-sort::before {
+  content: "\f0dc"; }
+
+.fa-unsorted::before {
+  content: "\f0dc"; }
+
+.fa-list-ol::before {
+  content: "\f0cb"; }
+
+.fa-list-1-2::before {
+  content: "\f0cb"; }
+
+.fa-list-numeric::before {
+  content: "\f0cb"; }
+
+.fa-person-dress-burst::before {
+  content: "\e544"; }
+
+.fa-money-check-dollar::before {
+  content: "\f53d"; }
+
+.fa-money-check-alt::before {
+  content: "\f53d"; }
+
+.fa-vector-square::before {
+  content: "\f5cb"; }
+
+.fa-bread-slice::before {
+  content: "\f7ec"; }
+
+.fa-language::before {
+  content: "\f1ab"; }
+
+.fa-face-kiss-wink-heart::before {
+  content: "\f598"; }
+
+.fa-kiss-wink-heart::before {
+  content: "\f598"; }
+
+.fa-filter::before {
+  content: "\f0b0"; }
+
+.fa-question::before {
+  content: "\3f"; }
+
+.fa-file-signature::before {
+  content: "\f573"; }
+
+.fa-up-down-left-right::before {
+  content: "\f0b2"; }
+
+.fa-arrows-alt::before {
+  content: "\f0b2"; }
+
+.fa-house-chimney-user::before {
+  content: "\e065"; }
+
+.fa-hand-holding-heart::before {
+  content: "\f4be"; }
+
+.fa-puzzle-piece::before {
+  content: "\f12e"; }
+
+.fa-money-check::before {
+  content: "\f53c"; }
+
+.fa-star-half-stroke::before {
+  content: "\f5c0"; }
+
+.fa-star-half-alt::before {
+  content: "\f5c0"; }
+
+.fa-code::before {
+  content: "\f121"; }
+
+.fa-whiskey-glass::before {
+  content: "\f7a0"; }
+
+.fa-glass-whiskey::before {
+  content: "\f7a0"; }
+
+.fa-building-circle-exclamation::before {
+  content: "\e4d3"; }
+
+.fa-magnifying-glass-chart::before {
+  content: "\e522"; }
+
+.fa-arrow-up-right-from-square::before {
+  content: "\f08e"; }
+
+.fa-external-link::before {
+  content: "\f08e"; }
+
+.fa-cubes-stacked::before {
+  content: "\e4e6"; }
+
+.fa-won-sign::before {
+  content: "\f159"; }
+
+.fa-krw::before {
+  content: "\f159"; }
+
+.fa-won::before {
+  content: "\f159"; }
+
+.fa-virus-covid::before {
+  content: "\e4a8"; }
+
+.fa-austral-sign::before {
+  content: "\e0a9"; }
+
+.fa-f::before {
+  content: "\46"; }
+
+.fa-leaf::before {
+  content: "\f06c"; }
+
+.fa-road::before {
+  content: "\f018"; }
+
+.fa-taxi::before {
+  content: "\f1ba"; }
+
+.fa-cab::before {
+  content: "\f1ba"; }
+
+.fa-person-circle-plus::before {
+  content: "\e541"; }
+
+.fa-chart-pie::before {
+  content: "\f200"; }
+
+.fa-pie-chart::before {
+  content: "\f200"; }
+
+.fa-bolt-lightning::before {
+  content: "\e0b7"; }
+
+.fa-sack-xmark::before {
+  content: "\e56a"; }
+
+.fa-file-excel::before {
+  content: "\f1c3"; }
+
+.fa-file-contract::before {
+  content: "\f56c"; }
+
+.fa-fish-fins::before {
+  content: "\e4f2"; }
+
+.fa-building-flag::before {
+  content: "\e4d5"; }
+
+.fa-face-grin-beam::before {
+  content: "\f582"; }
+
+.fa-grin-beam::before {
+  content: "\f582"; }
+
+.fa-object-ungroup::before {
+  content: "\f248"; }
+
+.fa-poop::before {
+  content: "\f619"; }
+
+.fa-location-pin::before {
+  content: "\f041"; }
+
+.fa-map-marker::before {
+  content: "\f041"; }
+
+.fa-kaaba::before {
+  content: "\f66b"; }
+
+.fa-toilet-paper::before {
+  content: "\f71e"; }
+
+.fa-helmet-safety::before {
+  content: "\f807"; }
+
+.fa-hard-hat::before {
+  content: "\f807"; }
+
+.fa-hat-hard::before {
+  content: "\f807"; }
+
+.fa-eject::before {
+  content: "\f052"; }
+
+.fa-circle-right::before {
+  content: "\f35a"; }
+
+.fa-arrow-alt-circle-right::before {
+  content: "\f35a"; }
+
+.fa-plane-circle-check::before {
+  content: "\e555"; }
+
+.fa-face-rolling-eyes::before {
+  content: "\f5a5"; }
+
+.fa-meh-rolling-eyes::before {
+  content: "\f5a5"; }
+
+.fa-object-group::before {
+  content: "\f247"; }
+
+.fa-chart-line::before {
+  content: "\f201"; }
+
+.fa-line-chart::before {
+  content: "\f201"; }
+
+.fa-mask-ventilator::before {
+  content: "\e524"; }
+
+.fa-arrow-right::before {
+  content: "\f061"; }
+
+.fa-signs-post::before {
+  content: "\f277"; }
+
+.fa-map-signs::before {
+  content: "\f277"; }
+
+.fa-cash-register::before {
+  content: "\f788"; }
+
+.fa-person-circle-question::before {
+  content: "\e542"; }
+
+.fa-h::before {
+  content: "\48"; }
+
+.fa-tarp::before {
+  content: "\e57b"; }
+
+.fa-screwdriver-wrench::before {
+  content: "\f7d9"; }
+
+.fa-tools::before {
+  content: "\f7d9"; }
+
+.fa-arrows-to-eye::before {
+  content: "\e4bf"; }
+
+.fa-plug-circle-bolt::before {
+  content: "\e55b"; }
+
+.fa-heart::before {
+  content: "\f004"; }
+
+.fa-mars-and-venus::before {
+  content: "\f224"; }
+
+.fa-house-user::before {
+  content: "\e1b0"; }
+
+.fa-home-user::before {
+  content: "\e1b0"; }
+
+.fa-dumpster-fire::before {
+  content: "\f794"; }
+
+.fa-house-crack::before {
+  content: "\e3b1"; }
+
+.fa-martini-glass-citrus::before {
+  content: "\f561"; }
+
+.fa-cocktail::before {
+  content: "\f561"; }
+
+.fa-face-surprise::before {
+  content: "\f5c2"; }
+
+.fa-surprise::before {
+  content: "\f5c2"; }
+
+.fa-bottle-water::before {
+  content: "\e4c5"; }
+
+.fa-circle-pause::before {
+  content: "\f28b"; }
+
+.fa-pause-circle::before {
+  content: "\f28b"; }
+
+.fa-toilet-paper-slash::before {
+  content: "\e072"; }
+
+.fa-apple-whole::before {
+  content: "\f5d1"; }
+
+.fa-apple-alt::before {
+  content: "\f5d1"; }
+
+.fa-kitchen-set::before {
+  content: "\e51a"; }
+
+.fa-r::before {
+  content: "\52"; }
+
+.fa-temperature-quarter::before {
+  content: "\f2ca"; }
+
+.fa-temperature-1::before {
+  content: "\f2ca"; }
+
+.fa-thermometer-1::before {
+  content: "\f2ca"; }
+
+.fa-thermometer-quarter::before {
+  content: "\f2ca"; }
+
+.fa-cube::before {
+  content: "\f1b2"; }
+
+.fa-bitcoin-sign::before {
+  content: "\e0b4"; }
+
+.fa-shield-dog::before {
+  content: "\e573"; }
+
+.fa-solar-panel::before {
+  content: "\f5ba"; }
+
+.fa-lock-open::before {
+  content: "\f3c1"; }
+
+.fa-elevator::before {
+  content: "\e16d"; }
+
+.fa-money-bill-transfer::before {
+  content: "\e528"; }
+
+.fa-money-bill-trend-up::before {
+  content: "\e529"; }
+
+.fa-house-flood-water-circle-arrow-right::before {
+  content: "\e50f"; }
+
+.fa-square-poll-horizontal::before {
+  content: "\f682"; }
+
+.fa-poll-h::before {
+  content: "\f682"; }
+
+.fa-circle::before {
+  content: "\f111"; }
+
+.fa-backward-fast::before {
+  content: "\f049"; }
+
+.fa-fast-backward::before {
+  content: "\f049"; }
+
+.fa-recycle::before {
+  content: "\f1b8"; }
+
+.fa-user-astronaut::before {
+  content: "\f4fb"; }
+
+.fa-plane-slash::before {
+  content: "\e069"; }
+
+.fa-trademark::before {
+  content: "\f25c"; }
+
+.fa-basketball::before {
+  content: "\f434"; }
+
+.fa-basketball-ball::before {
+  content: "\f434"; }
+
+.fa-satellite-dish::before {
+  content: "\f7c0"; }
+
+.fa-circle-up::before {
+  content: "\f35b"; }
+
+.fa-arrow-alt-circle-up::before {
+  content: "\f35b"; }
+
+.fa-mobile-screen-button::before {
+  content: "\f3cd"; }
+
+.fa-mobile-alt::before {
+  content: "\f3cd"; }
+
+.fa-volume-high::before {
+  content: "\f028"; }
+
+.fa-volume-up::before {
+  content: "\f028"; }
+
+.fa-users-rays::before {
+  content: "\e593"; }
+
+.fa-wallet::before {
+  content: "\f555"; }
+
+.fa-clipboard-check::before {
+  content: "\f46c"; }
+
+.fa-file-audio::before {
+  content: "\f1c7"; }
+
+.fa-burger::before {
+  content: "\f805"; }
+
+.fa-hamburger::before {
+  content: "\f805"; }
+
+.fa-wrench::before {
+  content: "\f0ad"; }
+
+.fa-bugs::before {
+  content: "\e4d0"; }
+
+.fa-rupee-sign::before {
+  content: "\f156"; }
+
+.fa-rupee::before {
+  content: "\f156"; }
+
+.fa-file-image::before {
+  content: "\f1c5"; }
+
+.fa-circle-question::before {
+  content: "\f059"; }
+
+.fa-question-circle::before {
+  content: "\f059"; }
+
+.fa-plane-departure::before {
+  content: "\f5b0"; }
+
+.fa-handshake-slash::before {
+  content: "\e060"; }
+
+.fa-book-bookmark::before {
+  content: "\e0bb"; }
+
+.fa-code-branch::before {
+  content: "\f126"; }
+
+.fa-hat-cowboy::before {
+  content: "\f8c0"; }
+
+.fa-bridge::before {
+  content: "\e4c8"; }
+
+.fa-phone-flip::before {
+  content: "\f879"; }
+
+.fa-phone-alt::before {
+  content: "\f879"; }
+
+.fa-truck-front::before {
+  content: "\e2b7"; }
+
+.fa-cat::before {
+  content: "\f6be"; }
+
+.fa-anchor-circle-exclamation::before {
+  content: "\e4ab"; }
+
+.fa-truck-field::before {
+  content: "\e58d"; }
+
+.fa-route::before {
+  content: "\f4d7"; }
+
+.fa-clipboard-question::before {
+  content: "\e4e3"; }
+
+.fa-panorama::before {
+  content: "\e209"; }
+
+.fa-comment-medical::before {
+  content: "\f7f5"; }
+
+.fa-teeth-open::before {
+  content: "\f62f"; }
+
+.fa-file-circle-minus::before {
+  content: "\e4ed"; }
+
+.fa-tags::before {
+  content: "\f02c"; }
+
+.fa-wine-glass::before {
+  content: "\f4e3"; }
+
+.fa-forward-fast::before {
+  content: "\f050"; }
+
+.fa-fast-forward::before {
+  content: "\f050"; }
+
+.fa-face-meh-blank::before {
+  content: "\f5a4"; }
+
+.fa-meh-blank::before {
+  content: "\f5a4"; }
+
+.fa-square-parking::before {
+  content: "\f540"; }
+
+.fa-parking::before {
+  content: "\f540"; }
+
+.fa-house-signal::before {
+  content: "\e012"; }
+
+.fa-bars-progress::before {
+  content: "\f828"; }
+
+.fa-tasks-alt::before {
+  content: "\f828"; }
+
+.fa-faucet-drip::before {
+  content: "\e006"; }
+
+.fa-cart-flatbed::before {
+  content: "\f474"; }
+
+.fa-dolly-flatbed::before {
+  content: "\f474"; }
+
+.fa-ban-smoking::before {
+  content: "\f54d"; }
+
+.fa-smoking-ban::before {
+  content: "\f54d"; }
+
+.fa-terminal::before {
+  content: "\f120"; }
+
+.fa-mobile-button::before {
+  content: "\f10b"; }
+
+.fa-house-medical-flag::before {
+  content: "\e514"; }
+
+.fa-basket-shopping::before {
+  content: "\f291"; }
+
+.fa-shopping-basket::before {
+  content: "\f291"; }
+
+.fa-tape::before {
+  content: "\f4db"; }
+
+.fa-bus-simple::before {
+  content: "\f55e"; }
+
+.fa-bus-alt::before {
+  content: "\f55e"; }
+
+.fa-eye::before {
+  content: "\f06e"; }
+
+.fa-face-sad-cry::before {
+  content: "\f5b3"; }
+
+.fa-sad-cry::before {
+  content: "\f5b3"; }
+
+.fa-audio-description::before {
+  content: "\f29e"; }
+
+.fa-person-military-to-person::before {
+  content: "\e54c"; }
+
+.fa-file-shield::before {
+  content: "\e4f0"; }
+
+.fa-user-slash::before {
+  content: "\f506"; }
+
+.fa-pen::before {
+  content: "\f304"; }
+
+.fa-tower-observation::before {
+  content: "\e586"; }
+
+.fa-file-code::before {
+  content: "\f1c9"; }
+
+.fa-signal::before {
+  content: "\f012"; }
+
+.fa-signal-5::before {
+  content: "\f012"; }
+
+.fa-signal-perfect::before {
+  content: "\f012"; }
+
+.fa-bus::before {
+  content: "\f207"; }
+
+.fa-heart-circle-xmark::before {
+  content: "\e501"; }
+
+.fa-house-chimney::before {
+  content: "\e3af"; }
+
+.fa-home-lg::before {
+  content: "\e3af"; }
+
+.fa-window-maximize::before {
+  content: "\f2d0"; }
+
+.fa-face-frown::before {
+  content: "\f119"; }
+
+.fa-frown::before {
+  content: "\f119"; }
+
+.fa-prescription::before {
+  content: "\f5b1"; }
+
+.fa-shop::before {
+  content: "\f54f"; }
+
+.fa-store-alt::before {
+  content: "\f54f"; }
+
+.fa-floppy-disk::before {
+  content: "\f0c7"; }
+
+.fa-save::before {
+  content: "\f0c7"; }
+
+.fa-vihara::before {
+  content: "\f6a7"; }
+
+.fa-scale-unbalanced::before {
+  content: "\f515"; }
+
+.fa-balance-scale-left::before {
+  content: "\f515"; }
+
+.fa-sort-up::before {
+  content: "\f0de"; }
+
+.fa-sort-asc::before {
+  content: "\f0de"; }
+
+.fa-comment-dots::before {
+  content: "\f4ad"; }
+
+.fa-commenting::before {
+  content: "\f4ad"; }
+
+.fa-plant-wilt::before {
+  content: "\e5aa"; }
+
+.fa-diamond::before {
+  content: "\f219"; }
+
+.fa-face-grin-squint::before {
+  content: "\f585"; }
+
+.fa-grin-squint::before {
+  content: "\f585"; }
+
+.fa-hand-holding-dollar::before {
+  content: "\f4c0"; }
+
+.fa-hand-holding-usd::before {
+  content: "\f4c0"; }
+
+.fa-bacterium::before {
+  content: "\e05a"; }
+
+.fa-hand-pointer::before {
+  content: "\f25a"; }
+
+.fa-drum-steelpan::before {
+  content: "\f56a"; }
+
+.fa-hand-scissors::before {
+  content: "\f257"; }
+
+.fa-hands-praying::before {
+  content: "\f684"; }
+
+.fa-praying-hands::before {
+  content: "\f684"; }
+
+.fa-arrow-rotate-right::before {
+  content: "\f01e"; }
+
+.fa-arrow-right-rotate::before {
+  content: "\f01e"; }
+
+.fa-arrow-rotate-forward::before {
+  content: "\f01e"; }
+
+.fa-redo::before {
+  content: "\f01e"; }
+
+.fa-biohazard::before {
+  content: "\f780"; }
+
+.fa-location-crosshairs::before {
+  content: "\f601"; }
+
+.fa-location::before {
+  content: "\f601"; }
+
+.fa-mars-double::before {
+  content: "\f227"; }
+
+.fa-child-dress::before {
+  content: "\e59c"; }
+
+.fa-users-between-lines::before {
+  content: "\e591"; }
+
+.fa-lungs-virus::before {
+  content: "\e067"; }
+
+.fa-face-grin-tears::before {
+  content: "\f588"; }
+
+.fa-grin-tears::before {
+  content: "\f588"; }
+
+.fa-phone::before {
+  content: "\f095"; }
+
+.fa-calendar-xmark::before {
+  content: "\f273"; }
+
+.fa-calendar-times::before {
+  content: "\f273"; }
+
+.fa-child-reaching::before {
+  content: "\e59d"; }
+
+.fa-head-side-virus::before {
+  content: "\e064"; }
+
+.fa-user-gear::before {
+  content: "\f4fe"; }
+
+.fa-user-cog::before {
+  content: "\f4fe"; }
+
+.fa-arrow-up-1-9::before {
+  content: "\f163"; }
+
+.fa-sort-numeric-up::before {
+  content: "\f163"; }
+
+.fa-door-closed::before {
+  content: "\f52a"; }
+
+.fa-shield-virus::before {
+  content: "\e06c"; }
+
+.fa-dice-six::before {
+  content: "\f526"; }
+
+.fa-mosquito-net::before {
+  content: "\e52c"; }
+
+.fa-bridge-water::before {
+  content: "\e4ce"; }
+
+.fa-person-booth::before {
+  content: "\f756"; }
+
+.fa-text-width::before {
+  content: "\f035"; }
+
+.fa-hat-wizard::before {
+  content: "\f6e8"; }
+
+.fa-pen-fancy::before {
+  content: "\f5ac"; }
+
+.fa-person-digging::before {
+  content: "\f85e"; }
+
+.fa-digging::before {
+  content: "\f85e"; }
+
+.fa-trash::before {
+  content: "\f1f8"; }
+
+.fa-gauge-simple::before {
+  content: "\f629"; }
+
+.fa-gauge-simple-med::before {
+  content: "\f629"; }
+
+.fa-tachometer-average::before {
+  content: "\f629"; }
+
+.fa-book-medical::before {
+  content: "\f7e6"; }
+
+.fa-poo::before {
+  content: "\f2fe"; }
+
+.fa-quote-right::before {
+  content: "\f10e"; }
+
+.fa-quote-right-alt::before {
+  content: "\f10e"; }
+
+.fa-shirt::before {
+  content: "\f553"; }
+
+.fa-t-shirt::before {
+  content: "\f553"; }
+
+.fa-tshirt::before {
+  content: "\f553"; }
+
+.fa-cubes::before {
+  content: "\f1b3"; }
+
+.fa-divide::before {
+  content: "\f529"; }
+
+.fa-tenge-sign::before {
+  content: "\f7d7"; }
+
+.fa-tenge::before {
+  content: "\f7d7"; }
+
+.fa-headphones::before {
+  content: "\f025"; }
+
+.fa-hands-holding::before {
+  content: "\f4c2"; }
+
+.fa-hands-clapping::before {
+  content: "\e1a8"; }
+
+.fa-republican::before {
+  content: "\f75e"; }
+
+.fa-arrow-left::before {
+  content: "\f060"; }
+
+.fa-person-circle-xmark::before {
+  content: "\e543"; }
+
+.fa-ruler::before {
+  content: "\f545"; }
+
+.fa-align-left::before {
+  content: "\f036"; }
+
+.fa-dice-d6::before {
+  content: "\f6d1"; }
+
+.fa-restroom::before {
+  content: "\f7bd"; }
+
+.fa-j::before {
+  content: "\4a"; }
+
+.fa-users-viewfinder::before {
+  content: "\e595"; }
+
+.fa-file-video::before {
+  content: "\f1c8"; }
+
+.fa-up-right-from-square::before {
+  content: "\f35d"; }
+
+.fa-external-link-alt::before {
+  content: "\f35d"; }
+
+.fa-table-cells::before {
+  content: "\f00a"; }
+
+.fa-th::before {
+  content: "\f00a"; }
+
+.fa-file-pdf::before {
+  content: "\f1c1"; }
+
+.fa-book-bible::before {
+  content: "\f647"; }
+
+.fa-bible::before {
+  content: "\f647"; }
+
+.fa-o::before {
+  content: "\4f"; }
+
+.fa-suitcase-medical::before {
+  content: "\f0fa"; }
+
+.fa-medkit::before {
+  content: "\f0fa"; }
+
+.fa-user-secret::before {
+  content: "\f21b"; }
+
+.fa-otter::before {
+  content: "\f700"; }
+
+.fa-person-dress::before {
+  content: "\f182"; }
+
+.fa-female::before {
+  content: "\f182"; }
+
+.fa-comment-dollar::before {
+  content: "\f651"; }
+
+.fa-business-time::before {
+  content: "\f64a"; }
+
+.fa-briefcase-clock::before {
+  content: "\f64a"; }
+
+.fa-table-cells-large::before {
+  content: "\f009"; }
+
+.fa-th-large::before {
+  content: "\f009"; }
+
+.fa-book-tanakh::before {
+  content: "\f827"; }
+
+.fa-tanakh::before {
+  content: "\f827"; }
+
+.fa-phone-volume::before {
+  content: "\f2a0"; }
+
+.fa-volume-control-phone::before {
+  content: "\f2a0"; }
+
+.fa-hat-cowboy-side::before {
+  content: "\f8c1"; }
+
+.fa-clipboard-user::before {
+  content: "\f7f3"; }
+
+.fa-child::before {
+  content: "\f1ae"; }
+
+.fa-lira-sign::before {
+  content: "\f195"; }
+
+.fa-satellite::before {
+  content: "\f7bf"; }
+
+.fa-plane-lock::before {
+  content: "\e558"; }
+
+.fa-tag::before {
+  content: "\f02b"; }
+
+.fa-comment::before {
+  content: "\f075"; }
+
+.fa-cake-candles::before {
+  content: "\f1fd"; }
+
+.fa-birthday-cake::before {
+  content: "\f1fd"; }
+
+.fa-cake::before {
+  content: "\f1fd"; }
+
+.fa-envelope::before {
+  content: "\f0e0"; }
+
+.fa-angles-up::before {
+  content: "\f102"; }
+
+.fa-angle-double-up::before {
+  content: "\f102"; }
+
+.fa-paperclip::before {
+  content: "\f0c6"; }
+
+.fa-arrow-right-to-city::before {
+  content: "\e4b3"; }
+
+.fa-ribbon::before {
+  content: "\f4d6"; }
+
+.fa-lungs::before {
+  content: "\f604"; }
+
+.fa-arrow-up-9-1::before {
+  content: "\f887"; }
+
+.fa-sort-numeric-up-alt::before {
+  content: "\f887"; }
+
+.fa-litecoin-sign::before {
+  content: "\e1d3"; }
+
+.fa-border-none::before {
+  content: "\f850"; }
+
+.fa-circle-nodes::before {
+  content: "\e4e2"; }
+
+.fa-parachute-box::before {
+  content: "\f4cd"; }
+
+.fa-indent::before {
+  content: "\f03c"; }
+
+.fa-truck-field-un::before {
+  content: "\e58e"; }
+
+.fa-hourglass::before {
+  content: "\f254"; }
+
+.fa-hourglass-empty::before {
+  content: "\f254"; }
+
+.fa-mountain::before {
+  content: "\f6fc"; }
+
+.fa-user-doctor::before {
+  content: "\f0f0"; }
+
+.fa-user-md::before {
+  content: "\f0f0"; }
+
+.fa-circle-info::before {
+  content: "\f05a"; }
+
+.fa-info-circle::before {
+  content: "\f05a"; }
+
+.fa-cloud-meatball::before {
+  content: "\f73b"; }
+
+.fa-camera::before {
+  content: "\f030"; }
+
+.fa-camera-alt::before {
+  content: "\f030"; }
+
+.fa-square-virus::before {
+  content: "\e578"; }
+
+.fa-meteor::before {
+  content: "\f753"; }
+
+.fa-car-on::before {
+  content: "\e4dd"; }
+
+.fa-sleigh::before {
+  content: "\f7cc"; }
+
+.fa-arrow-down-1-9::before {
+  content: "\f162"; }
+
+.fa-sort-numeric-asc::before {
+  content: "\f162"; }
+
+.fa-sort-numeric-down::before {
+  content: "\f162"; }
+
+.fa-hand-holding-droplet::before {
+  content: "\f4c1"; }
+
+.fa-hand-holding-water::before {
+  content: "\f4c1"; }
+
+.fa-water::before {
+  content: "\f773"; }
+
+.fa-calendar-check::before {
+  content: "\f274"; }
+
+.fa-braille::before {
+  content: "\f2a1"; }
+
+.fa-prescription-bottle-medical::before {
+  content: "\f486"; }
+
+.fa-prescription-bottle-alt::before {
+  content: "\f486"; }
+
+.fa-landmark::before {
+  content: "\f66f"; }
+
+.fa-truck::before {
+  content: "\f0d1"; }
+
+.fa-crosshairs::before {
+  content: "\f05b"; }
+
+.fa-person-cane::before {
+  content: "\e53c"; }
+
+.fa-tent::before {
+  content: "\e57d"; }
+
+.fa-vest-patches::before {
+  content: "\e086"; }
+
+.fa-check-double::before {
+  content: "\f560"; }
+
+.fa-arrow-down-a-z::before {
+  content: "\f15d"; }
+
+.fa-sort-alpha-asc::before {
+  content: "\f15d"; }
+
+.fa-sort-alpha-down::before {
+  content: "\f15d"; }
+
+.fa-money-bill-wheat::before {
+  content: "\e52a"; }
+
+.fa-cookie::before {
+  content: "\f563"; }
+
+.fa-arrow-rotate-left::before {
+  content: "\f0e2"; }
+
+.fa-arrow-left-rotate::before {
+  content: "\f0e2"; }
+
+.fa-arrow-rotate-back::before {
+  content: "\f0e2"; }
+
+.fa-arrow-rotate-backward::before {
+  content: "\f0e2"; }
+
+.fa-undo::before {
+  content: "\f0e2"; }
+
+.fa-hard-drive::before {
+  content: "\f0a0"; }
+
+.fa-hdd::before {
+  content: "\f0a0"; }
+
+.fa-face-grin-squint-tears::before {
+  content: "\f586"; }
+
+.fa-grin-squint-tears::before {
+  content: "\f586"; }
+
+.fa-dumbbell::before {
+  content: "\f44b"; }
+
+.fa-rectangle-list::before {
+  content: "\f022"; }
+
+.fa-list-alt::before {
+  content: "\f022"; }
+
+.fa-tarp-droplet::before {
+  content: "\e57c"; }
+
+.fa-house-medical-circle-check::before {
+  content: "\e511"; }
+
+.fa-person-skiing-nordic::before {
+  content: "\f7ca"; }
+
+.fa-skiing-nordic::before {
+  content: "\f7ca"; }
+
+.fa-calendar-plus::before {
+  content: "\f271"; }
+
+.fa-plane-arrival::before {
+  content: "\f5af"; }
+
+.fa-circle-left::before {
+  content: "\f359"; }
+
+.fa-arrow-alt-circle-left::before {
+  content: "\f359"; }
+
+.fa-train-subway::before {
+  content: "\f239"; }
+
+.fa-subway::before {
+  content: "\f239"; }
+
+.fa-chart-gantt::before {
+  content: "\e0e4"; }
+
+.fa-indian-rupee-sign::before {
+  content: "\e1bc"; }
+
+.fa-indian-rupee::before {
+  content: "\e1bc"; }
+
+.fa-inr::before {
+  content: "\e1bc"; }
+
+.fa-crop-simple::before {
+  content: "\f565"; }
+
+.fa-crop-alt::before {
+  content: "\f565"; }
+
+.fa-money-bill-1::before {
+  content: "\f3d1"; }
+
+.fa-money-bill-alt::before {
+  content: "\f3d1"; }
+
+.fa-left-long::before {
+  content: "\f30a"; }
+
+.fa-long-arrow-alt-left::before {
+  content: "\f30a"; }
+
+.fa-dna::before {
+  content: "\f471"; }
+
+.fa-virus-slash::before {
+  content: "\e075"; }
+
+.fa-minus::before {
+  content: "\f068"; }
+
+.fa-subtract::before {
+  content: "\f068"; }
+
+.fa-chess::before {
+  content: "\f439"; }
+
+.fa-arrow-left-long::before {
+  content: "\f177"; }
+
+.fa-long-arrow-left::before {
+  content: "\f177"; }
+
+.fa-plug-circle-check::before {
+  content: "\e55c"; }
+
+.fa-street-view::before {
+  content: "\f21d"; }
+
+.fa-franc-sign::before {
+  content: "\e18f"; }
+
+.fa-volume-off::before {
+  content: "\f026"; }
+
+.fa-hands-asl-interpreting::before {
+  content: "\f2a3"; }
+
+.fa-american-sign-language-interpreting::before {
+  content: "\f2a3"; }
+
+.fa-asl-interpreting::before {
+  content: "\f2a3"; }
+
+.fa-hands-american-sign-language-interpreting::before {
+  content: "\f2a3"; }
+
+.fa-gear::before {
+  content: "\f013"; }
+
+.fa-cog::before {
+  content: "\f013"; }
+
+.fa-droplet-slash::before {
+  content: "\f5c7"; }
+
+.fa-tint-slash::before {
+  content: "\f5c7"; }
+
+.fa-mosque::before {
+  content: "\f678"; }
+
+.fa-mosquito::before {
+  content: "\e52b"; }
+
+.fa-star-of-david::before {
+  content: "\f69a"; }
+
+.fa-person-military-rifle::before {
+  content: "\e54b"; }
+
+.fa-cart-shopping::before {
+  content: "\f07a"; }
+
+.fa-shopping-cart::before {
+  content: "\f07a"; }
+
+.fa-vials::before {
+  content: "\f493"; }
+
+.fa-plug-circle-plus::before {
+  content: "\e55f"; }
+
+.fa-place-of-worship::before {
+  content: "\f67f"; }
+
+.fa-grip-vertical::before {
+  content: "\f58e"; }
+
+.fa-arrow-turn-up::before {
+  content: "\f148"; }
+
+.fa-level-up::before {
+  content: "\f148"; }
+
+.fa-u::before {
+  content: "\55"; }
+
+.fa-square-root-variable::before {
+  content: "\f698"; }
+
+.fa-square-root-alt::before {
+  content: "\f698"; }
+
+.fa-clock::before {
+  content: "\f017"; }
+
+.fa-clock-four::before {
+  content: "\f017"; }
+
+.fa-backward-step::before {
+  content: "\f048"; }
+
+.fa-step-backward::before {
+  content: "\f048"; }
+
+.fa-pallet::before {
+  content: "\f482"; }
+
+.fa-faucet::before {
+  content: "\e005"; }
+
+.fa-baseball-bat-ball::before {
+  content: "\f432"; }
+
+.fa-s::before {
+  content: "\53"; }
+
+.fa-timeline::before {
+  content: "\e29c"; }
+
+.fa-keyboard::before {
+  content: "\f11c"; }
+
+.fa-caret-down::before {
+  content: "\f0d7"; }
+
+.fa-house-chimney-medical::before {
+  content: "\f7f2"; }
+
+.fa-clinic-medical::before {
+  content: "\f7f2"; }
+
+.fa-temperature-three-quarters::before {
+  content: "\f2c8"; }
+
+.fa-temperature-3::before {
+  content: "\f2c8"; }
+
+.fa-thermometer-3::before {
+  content: "\f2c8"; }
+
+.fa-thermometer-three-quarters::before {
+  content: "\f2c8"; }
+
+.fa-mobile-screen::before {
+  content: "\f3cf"; }
+
+.fa-mobile-android-alt::before {
+  content: "\f3cf"; }
+
+.fa-plane-up::before {
+  content: "\e22d"; }
+
+.fa-piggy-bank::before {
+  content: "\f4d3"; }
+
+.fa-battery-half::before {
+  content: "\f242"; }
+
+.fa-battery-3::before {
+  content: "\f242"; }
+
+.fa-mountain-city::before {
+  content: "\e52e"; }
+
+.fa-coins::before {
+  content: "\f51e"; }
+
+.fa-khanda::before {
+  content: "\f66d"; }
+
+.fa-sliders::before {
+  content: "\f1de"; }
+
+.fa-sliders-h::before {
+  content: "\f1de"; }
+
+.fa-folder-tree::before {
+  content: "\f802"; }
+
+.fa-network-wired::before {
+  content: "\f6ff"; }
+
+.fa-map-pin::before {
+  content: "\f276"; }
+
+.fa-hamsa::before {
+  content: "\f665"; }
+
+.fa-cent-sign::before {
+  content: "\e3f5"; }
+
+.fa-flask::before {
+  content: "\f0c3"; }
+
+.fa-person-pregnant::before {
+  content: "\e31e"; }
+
+.fa-wand-sparkles::before {
+  content: "\f72b"; }
+
+.fa-ellipsis-vertical::before {
+  content: "\f142"; }
+
+.fa-ellipsis-v::before {
+  content: "\f142"; }
+
+.fa-ticket::before {
+  content: "\f145"; }
+
+.fa-power-off::before {
+  content: "\f011"; }
+
+.fa-right-long::before {
+  content: "\f30b"; }
+
+.fa-long-arrow-alt-right::before {
+  content: "\f30b"; }
+
+.fa-flag-usa::before {
+  content: "\f74d"; }
+
+.fa-laptop-file::before {
+  content: "\e51d"; }
+
+.fa-tty::before {
+  content: "\f1e4"; }
+
+.fa-teletype::before {
+  content: "\f1e4"; }
+
+.fa-diagram-next::before {
+  content: "\e476"; }
+
+.fa-person-rifle::before {
+  content: "\e54e"; }
+
+.fa-house-medical-circle-exclamation::before {
+  content: "\e512"; }
+
+.fa-closed-captioning::before {
+  content: "\f20a"; }
+
+.fa-person-hiking::before {
+  content: "\f6ec"; }
+
+.fa-hiking::before {
+  content: "\f6ec"; }
+
+.fa-venus-double::before {
+  content: "\f226"; }
+
+.fa-images::before {
+  content: "\f302"; }
+
+.fa-calculator::before {
+  content: "\f1ec"; }
+
+.fa-people-pulling::before {
+  content: "\e535"; }
+
+.fa-n::before {
+  content: "\4e"; }
+
+.fa-cable-car::before {
+  content: "\f7da"; }
+
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+  content: "\f7da"; }
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+  content: "\f73d"; }
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+  content: "\e4d4"; }
+
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+  content: "\f21a"; }
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+  content: "\e4b8"; }
+
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+  content: "\f019"; }
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+  content: "\f580"; }
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+.fa-grin::before {
+  content: "\f580"; }
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+  content: "\f55a"; }
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+  content: "\f55a"; }
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+  content: "\f1fb"; }
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+  content: "\f1fb"; }
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+  content: "\f1fb"; }
+
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+  content: "\e5a0"; }
+
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+  content: "\f04e"; }
+
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+  content: "\f3ce"; }
+
+.fa-mobile-android::before {
+  content: "\f3ce"; }
+
+.fa-mobile-phone::before {
+  content: "\f3ce"; }
+
+.fa-face-meh::before {
+  content: "\f11a"; }
+
+.fa-meh::before {
+  content: "\f11a"; }
+
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+  content: "\f037"; }
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+  content: "\f6b7"; }
+
+.fa-book-dead::before {
+  content: "\f6b7"; }
+
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+  content: "\f2c2"; }
+
+.fa-drivers-license::before {
+  content: "\f2c2"; }
+
+.fa-outdent::before {
+  content: "\f03b"; }
+
+.fa-dedent::before {
+  content: "\f03b"; }
+
+.fa-heart-circle-exclamation::before {
+  content: "\e4fe"; }
+
+.fa-house::before {
+  content: "\f015"; }
+
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+  content: "\f015"; }
+
+.fa-home-alt::before {
+  content: "\f015"; }
+
+.fa-home-lg-alt::before {
+  content: "\f015"; }
+
+.fa-calendar-week::before {
+  content: "\f784"; }
+
+.fa-laptop-medical::before {
+  content: "\f812"; }
+
+.fa-b::before {
+  content: "\42"; }
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+.fa-file-medical::before {
+  content: "\f477"; }
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+.fa-dice-one::before {
+  content: "\f525"; }
+
+.fa-kiwi-bird::before {
+  content: "\f535"; }
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+  content: "\f0ec"; }
+
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+  content: "\f0ec"; }
+
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+  content: "\f2f9"; }
+
+.fa-redo-alt::before {
+  content: "\f2f9"; }
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+  content: "\f2f9"; }
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+  content: "\f2e7"; }
+
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+  content: "\f2e7"; }
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+  content: "\f161"; }
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+  content: "\f161"; }
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+.fa-mill-sign::before {
+  content: "\e1ed"; }
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+.fa-bowl-rice::before {
+  content: "\e2eb"; }
+
+.fa-skull::before {
+  content: "\f54c"; }
+
+.fa-tower-broadcast::before {
+  content: "\f519"; }
+
+.fa-broadcast-tower::before {
+  content: "\f519"; }
+
+.fa-truck-pickup::before {
+  content: "\f63c"; }
+
+.fa-up-long::before {
+  content: "\f30c"; }
+
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+  content: "\f30c"; }
+
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+  content: "\f04d"; }
+
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+  content: "\f387"; }
+
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+  content: "\f093"; }
+
+.fa-hurricane::before {
+  content: "\f751"; }
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+  content: "\e52d"; }
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+  content: "\e583"; }
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+  content: "\f51f"; }
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+  content: "\f56d"; }
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+  content: "\f56d"; }
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+  content: "\f8ff"; }
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+  content: "\e572"; }
+
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+  content: "\f0e7"; }
+
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+  content: "\f0e7"; }
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+  content: "\e4f4"; }
+
+.fa-oil-well::before {
+  content: "\e532"; }
+
+.fa-vault::before {
+  content: "\e2c5"; }
+
+.fa-mars::before {
+  content: "\f222"; }
+
+.fa-toilet::before {
+  content: "\f7d8"; }
+
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+  content: "\e557"; }
+
+.fa-yen-sign::before {
+  content: "\f157"; }
+
+.fa-cny::before {
+  content: "\f157"; }
+
+.fa-jpy::before {
+  content: "\f157"; }
+
+.fa-rmb::before {
+  content: "\f157"; }
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+  content: "\f157"; }
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+.fa-ruble-sign::before {
+  content: "\f158"; }
+
+.fa-rouble::before {
+  content: "\f158"; }
+
+.fa-rub::before {
+  content: "\f158"; }
+
+.fa-ruble::before {
+  content: "\f158"; }
+
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+  content: "\f185"; }
+
+.fa-guitar::before {
+  content: "\f7a6"; }
+
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+  content: "\f59c"; }
+
+.fa-laugh-wink::before {
+  content: "\f59c"; }
+
+.fa-horse-head::before {
+  content: "\f7ab"; }
+
+.fa-bore-hole::before {
+  content: "\e4c3"; }
+
+.fa-industry::before {
+  content: "\f275"; }
+
+.fa-circle-down::before {
+  content: "\f358"; }
+
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+  content: "\f358"; }
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+  content: "\e4c1"; }
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+  content: "\e184"; }
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+  content: "\f884"; }
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+.fa-sort-amount-desc::before {
+  content: "\f884"; }
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+  content: "\f884"; }
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+.fa-less-than::before {
+  content: "\3c"; }
+
+.fa-angle-down::before {
+  content: "\f107"; }
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+.fa-car-tunnel::before {
+  content: "\e4de"; }
+
+.fa-head-side-cough::before {
+  content: "\e061"; }
+
+.fa-grip-lines::before {
+  content: "\f7a4"; }
+
+.fa-thumbs-down::before {
+  content: "\f165"; }
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+.fa-user-lock::before {
+  content: "\f502"; }
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+.fa-arrow-right-long::before {
+  content: "\f178"; }
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+  content: "\f178"; }
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+  content: "\e4ac"; }
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+  content: "\f141"; }
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+  content: "\f141"; }
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+.fa-chess-pawn::before {
+  content: "\f443"; }
+
+.fa-kit-medical::before {
+  content: "\f479"; }
+
+.fa-first-aid::before {
+  content: "\f479"; }
+
+.fa-person-through-window::before {
+  content: "\e5a9"; }
+
+.fa-toolbox::before {
+  content: "\f552"; }
+
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+  content: "\e4fb"; }
+
+.fa-bug::before {
+  content: "\f188"; }
+
+.fa-credit-card::before {
+  content: "\f09d"; }
+
+.fa-credit-card-alt::before {
+  content: "\f09d"; }
+
+.fa-car::before {
+  content: "\f1b9"; }
+
+.fa-automobile::before {
+  content: "\f1b9"; }
+
+.fa-hand-holding-hand::before {
+  content: "\e4f7"; }
+
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+  content: "\f5da"; }
+
+.fa-book-reader::before {
+  content: "\f5da"; }
+
+.fa-mountain-sun::before {
+  content: "\e52f"; }
+
+.fa-arrows-left-right-to-line::before {
+  content: "\e4ba"; }
+
+.fa-dice-d20::before {
+  content: "\f6cf"; }
+
+.fa-truck-droplet::before {
+  content: "\e58c"; }
+
+.fa-file-circle-xmark::before {
+  content: "\e5a1"; }
+
+.fa-temperature-arrow-up::before {
+  content: "\e040"; }
+
+.fa-temperature-up::before {
+  content: "\e040"; }
+
+.fa-medal::before {
+  content: "\f5a2"; }
+
+.fa-bed::before {
+  content: "\f236"; }
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+.fa-square-h::before {
+  content: "\f0fd"; }
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+.fa-h-square::before {
+  content: "\f0fd"; }
+
+.fa-podcast::before {
+  content: "\f2ce"; }
+
+.fa-temperature-full::before {
+  content: "\f2c7"; }
+
+.fa-temperature-4::before {
+  content: "\f2c7"; }
+
+.fa-thermometer-4::before {
+  content: "\f2c7"; }
+
+.fa-thermometer-full::before {
+  content: "\f2c7"; }
+
+.fa-bell::before {
+  content: "\f0f3"; }
+
+.fa-superscript::before {
+  content: "\f12b"; }
+
+.fa-plug-circle-xmark::before {
+  content: "\e560"; }
+
+.fa-star-of-life::before {
+  content: "\f621"; }
+
+.fa-phone-slash::before {
+  content: "\f3dd"; }
+
+.fa-paint-roller::before {
+  content: "\f5aa"; }
+
+.fa-handshake-angle::before {
+  content: "\f4c4"; }
+
+.fa-hands-helping::before {
+  content: "\f4c4"; }
+
+.fa-location-dot::before {
+  content: "\f3c5"; }
+
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+  content: "\f3c5"; }
+
+.fa-file::before {
+  content: "\f15b"; }
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+.fa-greater-than::before {
+  content: "\3e"; }
+
+.fa-person-swimming::before {
+  content: "\f5c4"; }
+
+.fa-swimmer::before {
+  content: "\f5c4"; }
+
+.fa-arrow-down::before {
+  content: "\f063"; }
+
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+  content: "\f043"; }
+
+.fa-tint::before {
+  content: "\f043"; }
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+.fa-eraser::before {
+  content: "\f12d"; }
+
+.fa-earth-americas::before {
+  content: "\f57d"; }
+
+.fa-earth::before {
+  content: "\f57d"; }
+
+.fa-earth-america::before {
+  content: "\f57d"; }
+
+.fa-globe-americas::before {
+  content: "\f57d"; }
+
+.fa-person-burst::before {
+  content: "\e53b"; }
+
+.fa-dove::before {
+  content: "\f4ba"; }
+
+.fa-battery-empty::before {
+  content: "\f244"; }
+
+.fa-battery-0::before {
+  content: "\f244"; }
+
+.fa-socks::before {
+  content: "\f696"; }
+
+.fa-inbox::before {
+  content: "\f01c"; }
+
+.fa-section::before {
+  content: "\e447"; }
+
+.fa-gauge-high::before {
+  content: "\f625"; }
+
+.fa-tachometer-alt::before {
+  content: "\f625"; }
+
+.fa-tachometer-alt-fast::before {
+  content: "\f625"; }
+
+.fa-envelope-open-text::before {
+  content: "\f658"; }
+
+.fa-hospital::before {
+  content: "\f0f8"; }
+
+.fa-hospital-alt::before {
+  content: "\f0f8"; }
+
+.fa-hospital-wide::before {
+  content: "\f0f8"; }
+
+.fa-wine-bottle::before {
+  content: "\f72f"; }
+
+.fa-chess-rook::before {
+  content: "\f447"; }
+
+.fa-bars-staggered::before {
+  content: "\f550"; }
+
+.fa-reorder::before {
+  content: "\f550"; }
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+.fa-stream::before {
+  content: "\f550"; }
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+.fa-dharmachakra::before {
+  content: "\f655"; }
+
+.fa-hotdog::before {
+  content: "\f80f"; }
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+.fa-person-walking-with-cane::before {
+  content: "\f29d"; }
+
+.fa-blind::before {
+  content: "\f29d"; }
+
+.fa-drum::before {
+  content: "\f569"; }
+
+.fa-ice-cream::before {
+  content: "\f810"; }
+
+.fa-heart-circle-bolt::before {
+  content: "\e4fc"; }
+
+.fa-fax::before {
+  content: "\f1ac"; }
+
+.fa-paragraph::before {
+  content: "\f1dd"; }
+
+.fa-check-to-slot::before {
+  content: "\f772"; }
+
+.fa-vote-yea::before {
+  content: "\f772"; }
+
+.fa-star-half::before {
+  content: "\f089"; }
+
+.fa-boxes-stacked::before {
+  content: "\f468"; }
+
+.fa-boxes::before {
+  content: "\f468"; }
+
+.fa-boxes-alt::before {
+  content: "\f468"; }
+
+.fa-link::before {
+  content: "\f0c1"; }
+
+.fa-chain::before {
+  content: "\f0c1"; }
+
+.fa-ear-listen::before {
+  content: "\f2a2"; }
+
+.fa-assistive-listening-systems::before {
+  content: "\f2a2"; }
+
+.fa-tree-city::before {
+  content: "\e587"; }
+
+.fa-play::before {
+  content: "\f04b"; }
+
+.fa-font::before {
+  content: "\f031"; }
+
+.fa-rupiah-sign::before {
+  content: "\e23d"; }
+
+.fa-magnifying-glass::before {
+  content: "\f002"; }
+
+.fa-search::before {
+  content: "\f002"; }
+
+.fa-table-tennis-paddle-ball::before {
+  content: "\f45d"; }
+
+.fa-ping-pong-paddle-ball::before {
+  content: "\f45d"; }
+
+.fa-table-tennis::before {
+  content: "\f45d"; }
+
+.fa-person-dots-from-line::before {
+  content: "\f470"; }
+
+.fa-diagnoses::before {
+  content: "\f470"; }
+
+.fa-trash-can-arrow-up::before {
+  content: "\f82a"; }
+
+.fa-trash-restore-alt::before {
+  content: "\f82a"; }
+
+.fa-naira-sign::before {
+  content: "\e1f6"; }
+
+.fa-cart-arrow-down::before {
+  content: "\f218"; }
+
+.fa-walkie-talkie::before {
+  content: "\f8ef"; }
+
+.fa-file-pen::before {
+  content: "\f31c"; }
+
+.fa-file-edit::before {
+  content: "\f31c"; }
+
+.fa-receipt::before {
+  content: "\f543"; }
+
+.fa-square-pen::before {
+  content: "\f14b"; }
+
+.fa-pen-square::before {
+  content: "\f14b"; }
+
+.fa-pencil-square::before {
+  content: "\f14b"; }
+
+.fa-suitcase-rolling::before {
+  content: "\f5c1"; }
+
+.fa-person-circle-exclamation::before {
+  content: "\e53f"; }
+
+.fa-chevron-down::before {
+  content: "\f078"; }
+
+.fa-battery-full::before {
+  content: "\f240"; }
+
+.fa-battery::before {
+  content: "\f240"; }
+
+.fa-battery-5::before {
+  content: "\f240"; }
+
+.fa-skull-crossbones::before {
+  content: "\f714"; }
+
+.fa-code-compare::before {
+  content: "\e13a"; }
+
+.fa-list-ul::before {
+  content: "\f0ca"; }
+
+.fa-list-dots::before {
+  content: "\f0ca"; }
+
+.fa-school-lock::before {
+  content: "\e56f"; }
+
+.fa-tower-cell::before {
+  content: "\e585"; }
+
+.fa-down-long::before {
+  content: "\f309"; }
+
+.fa-long-arrow-alt-down::before {
+  content: "\f309"; }
+
+.fa-ranking-star::before {
+  content: "\e561"; }
+
+.fa-chess-king::before {
+  content: "\f43f"; }
+
+.fa-person-harassing::before {
+  content: "\e549"; }
+
+.fa-brazilian-real-sign::before {
+  content: "\e46c"; }
+
+.fa-landmark-dome::before {
+  content: "\f752"; }
+
+.fa-landmark-alt::before {
+  content: "\f752"; }
+
+.fa-arrow-up::before {
+  content: "\f062"; }
+
+.fa-tv::before {
+  content: "\f26c"; }
+
+.fa-television::before {
+  content: "\f26c"; }
+
+.fa-tv-alt::before {
+  content: "\f26c"; }
+
+.fa-shrimp::before {
+  content: "\e448"; }
+
+.fa-list-check::before {
+  content: "\f0ae"; }
+
+.fa-tasks::before {
+  content: "\f0ae"; }
+
+.fa-jug-detergent::before {
+  content: "\e519"; }
+
+.fa-circle-user::before {
+  content: "\f2bd"; }
+
+.fa-user-circle::before {
+  content: "\f2bd"; }
+
+.fa-user-shield::before {
+  content: "\f505"; }
+
+.fa-wind::before {
+  content: "\f72e"; }
+
+.fa-car-burst::before {
+  content: "\f5e1"; }
+
+.fa-car-crash::before {
+  content: "\f5e1"; }
+
+.fa-y::before {
+  content: "\59"; }
+
+.fa-person-snowboarding::before {
+  content: "\f7ce"; }
+
+.fa-snowboarding::before {
+  content: "\f7ce"; }
+
+.fa-truck-fast::before {
+  content: "\f48b"; }
+
+.fa-shipping-fast::before {
+  content: "\f48b"; }
+
+.fa-fish::before {
+  content: "\f578"; }
+
+.fa-user-graduate::before {
+  content: "\f501"; }
+
+.fa-circle-half-stroke::before {
+  content: "\f042"; }
+
+.fa-adjust::before {
+  content: "\f042"; }
+
+.fa-clapperboard::before {
+  content: "\e131"; }
+
+.fa-circle-radiation::before {
+  content: "\f7ba"; }
+
+.fa-radiation-alt::before {
+  content: "\f7ba"; }
+
+.fa-baseball::before {
+  content: "\f433"; }
+
+.fa-baseball-ball::before {
+  content: "\f433"; }
+
+.fa-jet-fighter-up::before {
+  content: "\e518"; }
+
+.fa-diagram-project::before {
+  content: "\f542"; }
+
+.fa-project-diagram::before {
+  content: "\f542"; }
+
+.fa-copy::before {
+  content: "\f0c5"; }
+
+.fa-volume-xmark::before {
+  content: "\f6a9"; }
+
+.fa-volume-mute::before {
+  content: "\f6a9"; }
+
+.fa-volume-times::before {
+  content: "\f6a9"; }
+
+.fa-hand-sparkles::before {
+  content: "\e05d"; }
+
+.fa-grip::before {
+  content: "\f58d"; }
+
+.fa-grip-horizontal::before {
+  content: "\f58d"; }
+
+.fa-share-from-square::before {
+  content: "\f14d"; }
+
+.fa-share-square::before {
+  content: "\f14d"; }
+
+.fa-child-combatant::before {
+  content: "\e4e0"; }
+
+.fa-child-rifle::before {
+  content: "\e4e0"; }
+
+.fa-gun::before {
+  content: "\e19b"; }
+
+.fa-square-phone::before {
+  content: "\f098"; }
+
+.fa-phone-square::before {
+  content: "\f098"; }
+
+.fa-plus::before {
+  content: "\2b"; }
+
+.fa-add::before {
+  content: "\2b"; }
+
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+  content: "\f065"; }
+
+.fa-computer::before {
+  content: "\e4e5"; }
+
+.fa-xmark::before {
+  content: "\f00d"; }
+
+.fa-close::before {
+  content: "\f00d"; }
+
+.fa-multiply::before {
+  content: "\f00d"; }
+
+.fa-remove::before {
+  content: "\f00d"; }
+
+.fa-times::before {
+  content: "\f00d"; }
+
+.fa-arrows-up-down-left-right::before {
+  content: "\f047"; }
+
+.fa-arrows::before {
+  content: "\f047"; }
+
+.fa-chalkboard-user::before {
+  content: "\f51c"; }
+
+.fa-chalkboard-teacher::before {
+  content: "\f51c"; }
+
+.fa-peso-sign::before {
+  content: "\e222"; }
+
+.fa-building-shield::before {
+  content: "\e4d8"; }
+
+.fa-baby::before {
+  content: "\f77c"; }
+
+.fa-users-line::before {
+  content: "\e592"; }
+
+.fa-quote-left::before {
+  content: "\f10d"; }
+
+.fa-quote-left-alt::before {
+  content: "\f10d"; }
+
+.fa-tractor::before {
+  content: "\f722"; }
+
+.fa-trash-arrow-up::before {
+  content: "\f829"; }
+
+.fa-trash-restore::before {
+  content: "\f829"; }
+
+.fa-arrow-down-up-lock::before {
+  content: "\e4b0"; }
+
+.fa-lines-leaning::before {
+  content: "\e51e"; }
+
+.fa-ruler-combined::before {
+  content: "\f546"; }
+
+.fa-copyright::before {
+  content: "\f1f9"; }
+
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+  content: "\3d"; }
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+  content: "\f517"; }
+
+.fa-teeth::before {
+  content: "\f62e"; }
+
+.fa-shekel-sign::before {
+  content: "\f20b"; }
+
+.fa-ils::before {
+  content: "\f20b"; }
+
+.fa-shekel::before {
+  content: "\f20b"; }
+
+.fa-sheqel::before {
+  content: "\f20b"; }
+
+.fa-sheqel-sign::before {
+  content: "\f20b"; }
+
+.fa-map::before {
+  content: "\f279"; }
+
+.fa-rocket::before {
+  content: "\f135"; }
+
+.fa-photo-film::before {
+  content: "\f87c"; }
+
+.fa-photo-video::before {
+  content: "\f87c"; }
+
+.fa-folder-minus::before {
+  content: "\f65d"; }
+
+.fa-store::before {
+  content: "\f54e"; }
+
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+  content: "\e098"; }
+
+.fa-plug-circle-minus::before {
+  content: "\e55e"; }
+
+.fa-sign-hanging::before {
+  content: "\f4d9"; }
+
+.fa-sign::before {
+  content: "\f4d9"; }
+
+.fa-bezier-curve::before {
+  content: "\f55b"; }
+
+.fa-bell-slash::before {
+  content: "\f1f6"; }
+
+.fa-tablet::before {
+  content: "\f3fb"; }
+
+.fa-tablet-android::before {
+  content: "\f3fb"; }
+
+.fa-school-flag::before {
+  content: "\e56e"; }
+
+.fa-fill::before {
+  content: "\f575"; }
+
+.fa-angle-up::before {
+  content: "\f106"; }
+
+.fa-drumstick-bite::before {
+  content: "\f6d7"; }
+
+.fa-holly-berry::before {
+  content: "\f7aa"; }
+
+.fa-chevron-left::before {
+  content: "\f053"; }
+
+.fa-bacteria::before {
+  content: "\e059"; }
+
+.fa-hand-lizard::before {
+  content: "\f258"; }
+
+.fa-notdef::before {
+  content: "\e1fe"; }
+
+.fa-disease::before {
+  content: "\f7fa"; }
+
+.fa-briefcase-medical::before {
+  content: "\f469"; }
+
+.fa-genderless::before {
+  content: "\f22d"; }
+
+.fa-chevron-right::before {
+  content: "\f054"; }
+
+.fa-retweet::before {
+  content: "\f079"; }
+
+.fa-car-rear::before {
+  content: "\f5de"; }
+
+.fa-car-alt::before {
+  content: "\f5de"; }
+
+.fa-pump-soap::before {
+  content: "\e06b"; }
+
+.fa-video-slash::before {
+  content: "\f4e2"; }
+
+.fa-battery-quarter::before {
+  content: "\f243"; }
+
+.fa-battery-2::before {
+  content: "\f243"; }
+
+.fa-radio::before {
+  content: "\f8d7"; }
+
+.fa-baby-carriage::before {
+  content: "\f77d"; }
+
+.fa-carriage-baby::before {
+  content: "\f77d"; }
+
+.fa-traffic-light::before {
+  content: "\f637"; }
+
+.fa-thermometer::before {
+  content: "\f491"; }
+
+.fa-vr-cardboard::before {
+  content: "\f729"; }
+
+.fa-hand-middle-finger::before {
+  content: "\f806"; }
+
+.fa-percent::before {
+  content: "\25"; }
+
+.fa-percentage::before {
+  content: "\25"; }
+
+.fa-truck-moving::before {
+  content: "\f4df"; }
+
+.fa-glass-water-droplet::before {
+  content: "\e4f5"; }
+
+.fa-display::before {
+  content: "\e163"; }
+
+.fa-face-smile::before {
+  content: "\f118"; }
+
+.fa-smile::before {
+  content: "\f118"; }
+
+.fa-thumbtack::before {
+  content: "\f08d"; }
+
+.fa-thumb-tack::before {
+  content: "\f08d"; }
+
+.fa-trophy::before {
+  content: "\f091"; }
+
+.fa-person-praying::before {
+  content: "\f683"; }
+
+.fa-pray::before {
+  content: "\f683"; }
+
+.fa-hammer::before {
+  content: "\f6e3"; }
+
+.fa-hand-peace::before {
+  content: "\f25b"; }
+
+.fa-rotate::before {
+  content: "\f2f1"; }
+
+.fa-sync-alt::before {
+  content: "\f2f1"; }
+
+.fa-spinner::before {
+  content: "\f110"; }
+
+.fa-robot::before {
+  content: "\f544"; }
+
+.fa-peace::before {
+  content: "\f67c"; }
+
+.fa-gears::before {
+  content: "\f085"; }
+
+.fa-cogs::before {
+  content: "\f085"; }
+
+.fa-warehouse::before {
+  content: "\f494"; }
+
+.fa-arrow-up-right-dots::before {
+  content: "\e4b7"; }
+
+.fa-splotch::before {
+  content: "\f5bc"; }
+
+.fa-face-grin-hearts::before {
+  content: "\f584"; }
+
+.fa-grin-hearts::before {
+  content: "\f584"; }
+
+.fa-dice-four::before {
+  content: "\f524"; }
+
+.fa-sim-card::before {
+  content: "\f7c4"; }
+
+.fa-transgender::before {
+  content: "\f225"; }
+
+.fa-transgender-alt::before {
+  content: "\f225"; }
+
+.fa-mercury::before {
+  content: "\f223"; }
+
+.fa-arrow-turn-down::before {
+  content: "\f149"; }
+
+.fa-level-down::before {
+  content: "\f149"; }
+
+.fa-person-falling-burst::before {
+  content: "\e547"; }
+
+.fa-award::before {
+  content: "\f559"; }
+
+.fa-ticket-simple::before {
+  content: "\f3ff"; }
+
+.fa-ticket-alt::before {
+  content: "\f3ff"; }
+
+.fa-building::before {
+  content: "\f1ad"; }
+
+.fa-angles-left::before {
+  content: "\f100"; }
+
+.fa-angle-double-left::before {
+  content: "\f100"; }
+
+.fa-qrcode::before {
+  content: "\f029"; }
+
+.fa-clock-rotate-left::before {
+  content: "\f1da"; }
+
+.fa-history::before {
+  content: "\f1da"; }
+
+.fa-face-grin-beam-sweat::before {
+  content: "\f583"; }
+
+.fa-grin-beam-sweat::before {
+  content: "\f583"; }
+
+.fa-file-export::before {
+  content: "\f56e"; }
+
+.fa-arrow-right-from-file::before {
+  content: "\f56e"; }
+
+.fa-shield::before {
+  content: "\f132"; }
+
+.fa-shield-blank::before {
+  content: "\f132"; }
+
+.fa-arrow-up-short-wide::before {
+  content: "\f885"; }
+
+.fa-sort-amount-up-alt::before {
+  content: "\f885"; }
+
+.fa-house-medical::before {
+  content: "\e3b2"; }
+
+.fa-golf-ball-tee::before {
+  content: "\f450"; }
+
+.fa-golf-ball::before {
+  content: "\f450"; }
+
+.fa-circle-chevron-left::before {
+  content: "\f137"; }
+
+.fa-chevron-circle-left::before {
+  content: "\f137"; }
+
+.fa-house-chimney-window::before {
+  content: "\e00d"; }
+
+.fa-pen-nib::before {
+  content: "\f5ad"; }
+
+.fa-tent-arrow-turn-left::before {
+  content: "\e580"; }
+
+.fa-tents::before {
+  content: "\e582"; }
+
+.fa-wand-magic::before {
+  content: "\f0d0"; }
+
+.fa-magic::before {
+  content: "\f0d0"; }
+
+.fa-dog::before {
+  content: "\f6d3"; }
+
+.fa-carrot::before {
+  content: "\f787"; }
+
+.fa-moon::before {
+  content: "\f186"; }
+
+.fa-wine-glass-empty::before {
+  content: "\f5ce"; }
+
+.fa-wine-glass-alt::before {
+  content: "\f5ce"; }
+
+.fa-cheese::before {
+  content: "\f7ef"; }
+
+.fa-yin-yang::before {
+  content: "\f6ad"; }
+
+.fa-music::before {
+  content: "\f001"; }
+
+.fa-code-commit::before {
+  content: "\f386"; }
+
+.fa-temperature-low::before {
+  content: "\f76b"; }
+
+.fa-person-biking::before {
+  content: "\f84a"; }
+
+.fa-biking::before {
+  content: "\f84a"; }
+
+.fa-broom::before {
+  content: "\f51a"; }
+
+.fa-shield-heart::before {
+  content: "\e574"; }
+
+.fa-gopuram::before {
+  content: "\f664"; }
+
+.fa-earth-oceania::before {
+  content: "\e47b"; }
+
+.fa-globe-oceania::before {
+  content: "\e47b"; }
+
+.fa-square-xmark::before {
+  content: "\f2d3"; }
+
+.fa-times-square::before {
+  content: "\f2d3"; }
+
+.fa-xmark-square::before {
+  content: "\f2d3"; }
+
+.fa-hashtag::before {
+  content: "\23"; }
+
+.fa-up-right-and-down-left-from-center::before {
+  content: "\f424"; }
+
+.fa-expand-alt::before {
+  content: "\f424"; }
+
+.fa-oil-can::before {
+  content: "\f613"; }
+
+.fa-t::before {
+  content: "\54"; }
+
+.fa-hippo::before {
+  content: "\f6ed"; }
+
+.fa-chart-column::before {
+  content: "\e0e3"; }
+
+.fa-infinity::before {
+  content: "\f534"; }
+
+.fa-vial-circle-check::before {
+  content: "\e596"; }
+
+.fa-person-arrow-down-to-line::before {
+  content: "\e538"; }
+
+.fa-voicemail::before {
+  content: "\f897"; }
+
+.fa-fan::before {
+  content: "\f863"; }
+
+.fa-person-walking-luggage::before {
+  content: "\e554"; }
+
+.fa-up-down::before {
+  content: "\f338"; }
+
+.fa-arrows-alt-v::before {
+  content: "\f338"; }
+
+.fa-cloud-moon-rain::before {
+  content: "\f73c"; }
+
+.fa-calendar::before {
+  content: "\f133"; }
+
+.fa-trailer::before {
+  content: "\e041"; }
+
+.fa-bahai::before {
+  content: "\f666"; }
+
+.fa-haykal::before {
+  content: "\f666"; }
+
+.fa-sd-card::before {
+  content: "\f7c2"; }
+
+.fa-dragon::before {
+  content: "\f6d5"; }
+
+.fa-shoe-prints::before {
+  content: "\f54b"; }
+
+.fa-circle-plus::before {
+  content: "\f055"; }
+
+.fa-plus-circle::before {
+  content: "\f055"; }
+
+.fa-face-grin-tongue-wink::before {
+  content: "\f58b"; }
+
+.fa-grin-tongue-wink::before {
+  content: "\f58b"; }
+
+.fa-hand-holding::before {
+  content: "\f4bd"; }
+
+.fa-plug-circle-exclamation::before {
+  content: "\e55d"; }
+
+.fa-link-slash::before {
+  content: "\f127"; }
+
+.fa-chain-broken::before {
+  content: "\f127"; }
+
+.fa-chain-slash::before {
+  content: "\f127"; }
+
+.fa-unlink::before {
+  content: "\f127"; }
+
+.fa-clone::before {
+  content: "\f24d"; }
+
+.fa-person-walking-arrow-loop-left::before {
+  content: "\e551"; }
+
+.fa-arrow-up-z-a::before {
+  content: "\f882"; }
+
+.fa-sort-alpha-up-alt::before {
+  content: "\f882"; }
+
+.fa-fire-flame-curved::before {
+  content: "\f7e4"; }
+
+.fa-fire-alt::before {
+  content: "\f7e4"; }
+
+.fa-tornado::before {
+  content: "\f76f"; }
+
+.fa-file-circle-plus::before {
+  content: "\e494"; }
+
+.fa-book-quran::before {
+  content: "\f687"; }
+
+.fa-quran::before {
+  content: "\f687"; }
+
+.fa-anchor::before {
+  content: "\f13d"; }
+
+.fa-border-all::before {
+  content: "\f84c"; }
+
+.fa-face-angry::before {
+  content: "\f556"; }
+
+.fa-angry::before {
+  content: "\f556"; }
+
+.fa-cookie-bite::before {
+  content: "\f564"; }
+
+.fa-arrow-trend-down::before {
+  content: "\e097"; }
+
+.fa-rss::before {
+  content: "\f09e"; }
+
+.fa-feed::before {
+  content: "\f09e"; }
+
+.fa-draw-polygon::before {
+  content: "\f5ee"; }
+
+.fa-scale-balanced::before {
+  content: "\f24e"; }
+
+.fa-balance-scale::before {
+  content: "\f24e"; }
+
+.fa-gauge-simple-high::before {
+  content: "\f62a"; }
+
+.fa-tachometer::before {
+  content: "\f62a"; }
+
+.fa-tachometer-fast::before {
+  content: "\f62a"; }
+
+.fa-shower::before {
+  content: "\f2cc"; }
+
+.fa-desktop::before {
+  content: "\f390"; }
+
+.fa-desktop-alt::before {
+  content: "\f390"; }
+
+.fa-m::before {
+  content: "\4d"; }
+
+.fa-table-list::before {
+  content: "\f00b"; }
+
+.fa-th-list::before {
+  content: "\f00b"; }
+
+.fa-comment-sms::before {
+  content: "\f7cd"; }
+
+.fa-sms::before {
+  content: "\f7cd"; }
+
+.fa-book::before {
+  content: "\f02d"; }
+
+.fa-user-plus::before {
+  content: "\f234"; }
+
+.fa-check::before {
+  content: "\f00c"; }
+
+.fa-battery-three-quarters::before {
+  content: "\f241"; }
+
+.fa-battery-4::before {
+  content: "\f241"; }
+
+.fa-house-circle-check::before {
+  content: "\e509"; }
+
+.fa-angle-left::before {
+  content: "\f104"; }
+
+.fa-diagram-successor::before {
+  content: "\e47a"; }
+
+.fa-truck-arrow-right::before {
+  content: "\e58b"; }
+
+.fa-arrows-split-up-and-left::before {
+  content: "\e4bc"; }
+
+.fa-hand-fist::before {
+  content: "\f6de"; }
+
+.fa-fist-raised::before {
+  content: "\f6de"; }
+
+.fa-cloud-moon::before {
+  content: "\f6c3"; }
+
+.fa-briefcase::before {
+  content: "\f0b1"; }
+
+.fa-person-falling::before {
+  content: "\e546"; }
+
+.fa-image-portrait::before {
+  content: "\f3e0"; }
+
+.fa-portrait::before {
+  content: "\f3e0"; }
+
+.fa-user-tag::before {
+  content: "\f507"; }
+
+.fa-rug::before {
+  content: "\e569"; }
+
+.fa-earth-europe::before {
+  content: "\f7a2"; }
+
+.fa-globe-europe::before {
+  content: "\f7a2"; }
+
+.fa-cart-flatbed-suitcase::before {
+  content: "\f59d"; }
+
+.fa-luggage-cart::before {
+  content: "\f59d"; }
+
+.fa-rectangle-xmark::before {
+  content: "\f410"; }
+
+.fa-rectangle-times::before {
+  content: "\f410"; }
+
+.fa-times-rectangle::before {
+  content: "\f410"; }
+
+.fa-window-close::before {
+  content: "\f410"; }
+
+.fa-baht-sign::before {
+  content: "\e0ac"; }
+
+.fa-book-open::before {
+  content: "\f518"; }
+
+.fa-book-journal-whills::before {
+  content: "\f66a"; }
+
+.fa-journal-whills::before {
+  content: "\f66a"; }
+
+.fa-handcuffs::before {
+  content: "\e4f8"; }
+
+.fa-triangle-exclamation::before {
+  content: "\f071"; }
+
+.fa-exclamation-triangle::before {
+  content: "\f071"; }
+
+.fa-warning::before {
+  content: "\f071"; }
+
+.fa-database::before {
+  content: "\f1c0"; }
+
+.fa-share::before {
+  content: "\f064"; }
+
+.fa-arrow-turn-right::before {
+  content: "\f064"; }
+
+.fa-mail-forward::before {
+  content: "\f064"; }
+
+.fa-bottle-droplet::before {
+  content: "\e4c4"; }
+
+.fa-mask-face::before {
+  content: "\e1d7"; }
+
+.fa-hill-rockslide::before {
+  content: "\e508"; }
+
+.fa-right-left::before {
+  content: "\f362"; }
+
+.fa-exchange-alt::before {
+  content: "\f362"; }
+
+.fa-paper-plane::before {
+  content: "\f1d8"; }
+
+.fa-road-circle-exclamation::before {
+  content: "\e565"; }
+
+.fa-dungeon::before {
+  content: "\f6d9"; }
+
+.fa-align-right::before {
+  content: "\f038"; }
+
+.fa-money-bill-1-wave::before {
+  content: "\f53b"; }
+
+.fa-money-bill-wave-alt::before {
+  content: "\f53b"; }
+
+.fa-life-ring::before {
+  content: "\f1cd"; }
+
+.fa-hands::before {
+  content: "\f2a7"; }
+
+.fa-sign-language::before {
+  content: "\f2a7"; }
+
+.fa-signing::before {
+  content: "\f2a7"; }
+
+.fa-calendar-day::before {
+  content: "\f783"; }
+
+.fa-water-ladder::before {
+  content: "\f5c5"; }
+
+.fa-ladder-water::before {
+  content: "\f5c5"; }
+
+.fa-swimming-pool::before {
+  content: "\f5c5"; }
+
+.fa-arrows-up-down::before {
+  content: "\f07d"; }
+
+.fa-arrows-v::before {
+  content: "\f07d"; }
+
+.fa-face-grimace::before {
+  content: "\f57f"; }
+
+.fa-grimace::before {
+  content: "\f57f"; }
+
+.fa-wheelchair-move::before {
+  content: "\e2ce"; }
+
+.fa-wheelchair-alt::before {
+  content: "\e2ce"; }
+
+.fa-turn-down::before {
+  content: "\f3be"; }
+
+.fa-level-down-alt::before {
+  content: "\f3be"; }
+
+.fa-person-walking-arrow-right::before {
+  content: "\e552"; }
+
+.fa-square-envelope::before {
+  content: "\f199"; }
+
+.fa-envelope-square::before {
+  content: "\f199"; }
+
+.fa-dice::before {
+  content: "\f522"; }
+
+.fa-bowling-ball::before {
+  content: "\f436"; }
+
+.fa-brain::before {
+  content: "\f5dc"; }
+
+.fa-bandage::before {
+  content: "\f462"; }
+
+.fa-band-aid::before {
+  content: "\f462"; }
+
+.fa-calendar-minus::before {
+  content: "\f272"; }
+
+.fa-circle-xmark::before {
+  content: "\f057"; }
+
+.fa-times-circle::before {
+  content: "\f057"; }
+
+.fa-xmark-circle::before {
+  content: "\f057"; }
+
+.fa-gifts::before {
+  content: "\f79c"; }
+
+.fa-hotel::before {
+  content: "\f594"; }
+
+.fa-earth-asia::before {
+  content: "\f57e"; }
+
+.fa-globe-asia::before {
+  content: "\f57e"; }
+
+.fa-id-card-clip::before {
+  content: "\f47f"; }
+
+.fa-id-card-alt::before {
+  content: "\f47f"; }
+
+.fa-magnifying-glass-plus::before {
+  content: "\f00e"; }
+
+.fa-search-plus::before {
+  content: "\f00e"; }
+
+.fa-thumbs-up::before {
+  content: "\f164"; }
+
+.fa-user-clock::before {
+  content: "\f4fd"; }
+
+.fa-hand-dots::before {
+  content: "\f461"; }
+
+.fa-allergies::before {
+  content: "\f461"; }
+
+.fa-file-invoice::before {
+  content: "\f570"; }
+
+.fa-window-minimize::before {
+  content: "\f2d1"; }
+
+.fa-mug-saucer::before {
+  content: "\f0f4"; }
+
+.fa-coffee::before {
+  content: "\f0f4"; }
+
+.fa-brush::before {
+  content: "\f55d"; }
+
+.fa-mask::before {
+  content: "\f6fa"; }
+
+.fa-magnifying-glass-minus::before {
+  content: "\f010"; }
+
+.fa-search-minus::before {
+  content: "\f010"; }
+
+.fa-ruler-vertical::before {
+  content: "\f548"; }
+
+.fa-user-large::before {
+  content: "\f406"; }
+
+.fa-user-alt::before {
+  content: "\f406"; }
+
+.fa-train-tram::before {
+  content: "\e5b4"; }
+
+.fa-user-nurse::before {
+  content: "\f82f"; }
+
+.fa-syringe::before {
+  content: "\f48e"; }
+
+.fa-cloud-sun::before {
+  content: "\f6c4"; }
+
+.fa-stopwatch-20::before {
+  content: "\e06f"; }
+
+.fa-square-full::before {
+  content: "\f45c"; }
+
+.fa-magnet::before {
+  content: "\f076"; }
+
+.fa-jar::before {
+  content: "\e516"; }
+
+.fa-note-sticky::before {
+  content: "\f249"; }
+
+.fa-sticky-note::before {
+  content: "\f249"; }
+
+.fa-bug-slash::before {
+  content: "\e490"; }
+
+.fa-arrow-up-from-water-pump::before {
+  content: "\e4b6"; }
+
+.fa-bone::before {
+  content: "\f5d7"; }
+
+.fa-user-injured::before {
+  content: "\f728"; }
+
+.fa-face-sad-tear::before {
+  content: "\f5b4"; }
+
+.fa-sad-tear::before {
+  content: "\f5b4"; }
+
+.fa-plane::before {
+  content: "\f072"; }
+
+.fa-tent-arrows-down::before {
+  content: "\e581"; }
+
+.fa-exclamation::before {
+  content: "\21"; }
+
+.fa-arrows-spin::before {
+  content: "\e4bb"; }
+
+.fa-print::before {
+  content: "\f02f"; }
+
+.fa-turkish-lira-sign::before {
+  content: "\e2bb"; }
+
+.fa-try::before {
+  content: "\e2bb"; }
+
+.fa-turkish-lira::before {
+  content: "\e2bb"; }
+
+.fa-dollar-sign::before {
+  content: "\24"; }
+
+.fa-dollar::before {
+  content: "\24"; }
+
+.fa-usd::before {
+  content: "\24"; }
+
+.fa-x::before {
+  content: "\58"; }
+
+.fa-magnifying-glass-dollar::before {
+  content: "\f688"; }
+
+.fa-search-dollar::before {
+  content: "\f688"; }
+
+.fa-users-gear::before {
+  content: "\f509"; }
+
+.fa-users-cog::before {
+  content: "\f509"; }
+
+.fa-person-military-pointing::before {
+  content: "\e54a"; }
+
+.fa-building-columns::before {
+  content: "\f19c"; }
+
+.fa-bank::before {
+  content: "\f19c"; }
+
+.fa-institution::before {
+  content: "\f19c"; }
+
+.fa-museum::before {
+  content: "\f19c"; }
+
+.fa-university::before {
+  content: "\f19c"; }
+
+.fa-umbrella::before {
+  content: "\f0e9"; }
+
+.fa-trowel::before {
+  content: "\e589"; }
+
+.fa-d::before {
+  content: "\44"; }
+
+.fa-stapler::before {
+  content: "\e5af"; }
+
+.fa-masks-theater::before {
+  content: "\f630"; }
+
+.fa-theater-masks::before {
+  content: "\f630"; }
+
+.fa-kip-sign::before {
+  content: "\e1c4"; }
+
+.fa-hand-point-left::before {
+  content: "\f0a5"; }
+
+.fa-handshake-simple::before {
+  content: "\f4c6"; }
+
+.fa-handshake-alt::before {
+  content: "\f4c6"; }
+
+.fa-jet-fighter::before {
+  content: "\f0fb"; }
+
+.fa-fighter-jet::before {
+  content: "\f0fb"; }
+
+.fa-square-share-nodes::before {
+  content: "\f1e1"; }
+
+.fa-share-alt-square::before {
+  content: "\f1e1"; }
+
+.fa-barcode::before {
+  content: "\f02a"; }
+
+.fa-plus-minus::before {
+  content: "\e43c"; }
+
+.fa-video::before {
+  content: "\f03d"; }
+
+.fa-video-camera::before {
+  content: "\f03d"; }
+
+.fa-graduation-cap::before {
+  content: "\f19d"; }
+
+.fa-mortar-board::before {
+  content: "\f19d"; }
+
+.fa-hand-holding-medical::before {
+  content: "\e05c"; }
+
+.fa-person-circle-check::before {
+  content: "\e53e"; }
+
+.fa-turn-up::before {
+  content: "\f3bf"; }
+
+.fa-level-up-alt::before {
+  content: "\f3bf"; }
+
+.sr-only,
+.fa-sr-only {
+  position: absolute;
+  width: 1px;
+  height: 1px;
+  padding: 0;
+  margin: -1px;
+  overflow: hidden;
+  clip: rect(0, 0, 0, 0);
+  white-space: nowrap;
+  border-width: 0; }
+
+.sr-only-focusable:not(:focus),
+.fa-sr-only-focusable:not(:focus) {
+  position: absolute;
+  width: 1px;
+  height: 1px;
+  padding: 0;
+  margin: -1px;
+  overflow: hidden;
+  clip: rect(0, 0, 0, 0);
+  white-space: nowrap;
+  border-width: 0; }
+:root, :host {
+  --fa-style-family-brands: 'Font Awesome 6 Brands';
+  --fa-font-brands: normal 400 1em/1 'Font Awesome 6 Brands'; }
+
+@font-face {
+  font-family: 'Font Awesome 6 Brands';
+  font-style: normal;
+  font-weight: 400;
+  font-display: block;
+  src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); }
+
+.fab,
+.fa-brands {
+  font-weight: 400; }
+
+.fa-monero:before {
+  content: "\f3d0"; }
+
+.fa-hooli:before {
+  content: "\f427"; }
+
+.fa-yelp:before {
+  content: "\f1e9"; }
+
+.fa-cc-visa:before {
+  content: "\f1f0"; }
+
+.fa-lastfm:before {
+  content: "\f202"; }
+
+.fa-shopware:before {
+  content: "\f5b5"; }
+
+.fa-creative-commons-nc:before {
+  content: "\f4e8"; }
+
+.fa-aws:before {
+  content: "\f375"; }
+
+.fa-redhat:before {
+  content: "\f7bc"; }
+
+.fa-yoast:before {
+  content: "\f2b1"; }
+
+.fa-cloudflare:before {
+  content: "\e07d"; }
+
+.fa-ups:before {
+  content: "\f7e0"; }
+
+.fa-wpexplorer:before {
+  content: "\f2de"; }
+
+.fa-dyalog:before {
+  content: "\f399"; }
+
+.fa-bity:before {
+  content: "\f37a"; }
+
+.fa-stackpath:before {
+  content: "\f842"; }
+
+.fa-buysellads:before {
+  content: "\f20d"; }
+
+.fa-first-order:before {
+  content: "\f2b0"; }
+
+.fa-modx:before {
+  content: "\f285"; }
+
+.fa-guilded:before {
+  content: "\e07e"; }
+
+.fa-vnv:before {
+  content: "\f40b"; }
+
+.fa-square-js:before {
+  content: "\f3b9"; }
+
+.fa-js-square:before {
+  content: "\f3b9"; }
+
+.fa-microsoft:before {
+  content: "\f3ca"; }
+
+.fa-qq:before {
+  content: "\f1d6"; }
+
+.fa-orcid:before {
+  content: "\f8d2"; }
+
+.fa-java:before {
+  content: "\f4e4"; }
+
+.fa-invision:before {
+  content: "\f7b0"; }
+
+.fa-creative-commons-pd-alt:before {
+  content: "\f4ed"; }
+
+.fa-centercode:before {
+  content: "\f380"; }
+
+.fa-glide-g:before {
+  content: "\f2a6"; }
+
+.fa-drupal:before {
+  content: "\f1a9"; }
+
+.fa-hire-a-helper:before {
+  content: "\f3b0"; }
+
+.fa-creative-commons-by:before {
+  content: "\f4e7"; }
+
+.fa-unity:before {
+  content: "\e049"; }
+
+.fa-whmcs:before {
+  content: "\f40d"; }
+
+.fa-rocketchat:before {
+  content: "\f3e8"; }
+
+.fa-vk:before {
+  content: "\f189"; }
+
+.fa-untappd:before {
+  content: "\f405"; }
+
+.fa-mailchimp:before {
+  content: "\f59e"; }
+
+.fa-css3-alt:before {
+  content: "\f38b"; }
+
+.fa-square-reddit:before {
+  content: "\f1a2"; }
+
+.fa-reddit-square:before {
+  content: "\f1a2"; }
+
+.fa-vimeo-v:before {
+  content: "\f27d"; }
+
+.fa-contao:before {
+  content: "\f26d"; }
+
+.fa-square-font-awesome:before {
+  content: "\e5ad"; }
+
+.fa-deskpro:before {
+  content: "\f38f"; }
+
+.fa-sistrix:before {
+  content: "\f3ee"; }
+
+.fa-square-instagram:before {
+  content: "\e055"; }
+
+.fa-instagram-square:before {
+  content: "\e055"; }
+
+.fa-battle-net:before {
+  content: "\f835"; }
+
+.fa-the-red-yeti:before {
+  content: "\f69d"; }
+
+.fa-square-hacker-news:before {
+  content: "\f3af"; }
+
+.fa-hacker-news-square:before {
+  content: "\f3af"; }
+
+.fa-edge:before {
+  content: "\f282"; }
+
+.fa-threads:before {
+  content: "\e618"; }
+
+.fa-napster:before {
+  content: "\f3d2"; }
+
+.fa-square-snapchat:before {
+  content: "\f2ad"; }
+
+.fa-snapchat-square:before {
+  content: "\f2ad"; }
+
+.fa-google-plus-g:before {
+  content: "\f0d5"; }
+
+.fa-artstation:before {
+  content: "\f77a"; }
+
+.fa-markdown:before {
+  content: "\f60f"; }
+
+.fa-sourcetree:before {
+  content: "\f7d3"; }
+
+.fa-google-plus:before {
+  content: "\f2b3"; }
+
+.fa-diaspora:before {
+  content: "\f791"; }
+
+.fa-foursquare:before {
+  content: "\f180"; }
+
+.fa-stack-overflow:before {
+  content: "\f16c"; }
+
+.fa-github-alt:before {
+  content: "\f113"; }
+
+.fa-phoenix-squadron:before {
+  content: "\f511"; }
+
+.fa-pagelines:before {
+  content: "\f18c"; }
+
+.fa-algolia:before {
+  content: "\f36c"; }
+
+.fa-red-river:before {
+  content: "\f3e3"; }
+
+.fa-creative-commons-sa:before {
+  content: "\f4ef"; }
+
+.fa-safari:before {
+  content: "\f267"; }
+
+.fa-google:before {
+  content: "\f1a0"; }
+
+.fa-square-font-awesome-stroke:before {
+  content: "\f35c"; }
+
+.fa-font-awesome-alt:before {
+  content: "\f35c"; }
+
+.fa-atlassian:before {
+  content: "\f77b"; }
+
+.fa-linkedin-in:before {
+  content: "\f0e1"; }
+
+.fa-digital-ocean:before {
+  content: "\f391"; }
+
+.fa-nimblr:before {
+  content: "\f5a8"; }
+
+.fa-chromecast:before {
+  content: "\f838"; }
+
+.fa-evernote:before {
+  content: "\f839"; }
+
+.fa-hacker-news:before {
+  content: "\f1d4"; }
+
+.fa-creative-commons-sampling:before {
+  content: "\f4f0"; }
+
+.fa-adversal:before {
+  content: "\f36a"; }
+
+.fa-creative-commons:before {
+  content: "\f25e"; }
+
+.fa-watchman-monitoring:before {
+  content: "\e087"; }
+
+.fa-fonticons:before {
+  content: "\f280"; }
+
+.fa-weixin:before {
+  content: "\f1d7"; }
+
+.fa-shirtsinbulk:before {
+  content: "\f214"; }
+
+.fa-codepen:before {
+  content: "\f1cb"; }
+
+.fa-git-alt:before {
+  content: "\f841"; }
+
+.fa-lyft:before {
+  content: "\f3c3"; }
+
+.fa-rev:before {
+  content: "\f5b2"; }
+
+.fa-windows:before {
+  content: "\f17a"; }
+
+.fa-wizards-of-the-coast:before {
+  content: "\f730"; }
+
+.fa-square-viadeo:before {
+  content: "\f2aa"; }
+
+.fa-viadeo-square:before {
+  content: "\f2aa"; }
+
+.fa-meetup:before {
+  content: "\f2e0"; }
+
+.fa-centos:before {
+  content: "\f789"; }
+
+.fa-adn:before {
+  content: "\f170"; }
+
+.fa-cloudsmith:before {
+  content: "\f384"; }
+
+.fa-pied-piper-alt:before {
+  content: "\f1a8"; }
+
+.fa-square-dribbble:before {
+  content: "\f397"; }
+
+.fa-dribbble-square:before {
+  content: "\f397"; }
+
+.fa-codiepie:before {
+  content: "\f284"; }
+
+.fa-node:before {
+  content: "\f419"; }
+
+.fa-mix:before {
+  content: "\f3cb"; }
+
+.fa-steam:before {
+  content: "\f1b6"; }
+
+.fa-cc-apple-pay:before {
+  content: "\f416"; }
+
+.fa-scribd:before {
+  content: "\f28a"; }
+
+.fa-debian:before {
+  content: "\e60b"; }
+
+.fa-openid:before {
+  content: "\f19b"; }
+
+.fa-instalod:before {
+  content: "\e081"; }
+
+.fa-expeditedssl:before {
+  content: "\f23e"; }
+
+.fa-sellcast:before {
+  content: "\f2da"; }
+
+.fa-square-twitter:before {
+  content: "\f081"; }
+
+.fa-twitter-square:before {
+  content: "\f081"; }
+
+.fa-r-project:before {
+  content: "\f4f7"; }
+
+.fa-delicious:before {
+  content: "\f1a5"; }
+
+.fa-freebsd:before {
+  content: "\f3a4"; }
+
+.fa-vuejs:before {
+  content: "\f41f"; }
+
+.fa-accusoft:before {
+  content: "\f369"; }
+
+.fa-ioxhost:before {
+  content: "\f208"; }
+
+.fa-fonticons-fi:before {
+  content: "\f3a2"; }
+
+.fa-app-store:before {
+  content: "\f36f"; }
+
+.fa-cc-mastercard:before {
+  content: "\f1f1"; }
+
+.fa-itunes-note:before {
+  content: "\f3b5"; }
+
+.fa-golang:before {
+  content: "\e40f"; }
+
+.fa-kickstarter:before {
+  content: "\f3bb"; }
+
+.fa-grav:before {
+  content: "\f2d6"; }
+
+.fa-weibo:before {
+  content: "\f18a"; }
+
+.fa-uncharted:before {
+  content: "\e084"; }
+
+.fa-firstdraft:before {
+  content: "\f3a1"; }
+
+.fa-square-youtube:before {
+  content: "\f431"; }
+
+.fa-youtube-square:before {
+  content: "\f431"; }
+
+.fa-wikipedia-w:before {
+  content: "\f266"; }
+
+.fa-wpressr:before {
+  content: "\f3e4"; }
+
+.fa-rendact:before {
+  content: "\f3e4"; }
+
+.fa-angellist:before {
+  content: "\f209"; }
+
+.fa-galactic-republic:before {
+  content: "\f50c"; }
+
+.fa-nfc-directional:before {
+  content: "\e530"; }
+
+.fa-skype:before {
+  content: "\f17e"; }
+
+.fa-joget:before {
+  content: "\f3b7"; }
+
+.fa-fedora:before {
+  content: "\f798"; }
+
+.fa-stripe-s:before {
+  content: "\f42a"; }
+
+.fa-meta:before {
+  content: "\e49b"; }
+
+.fa-laravel:before {
+  content: "\f3bd"; }
+
+.fa-hotjar:before {
+  content: "\f3b1"; }
+
+.fa-bluetooth-b:before {
+  content: "\f294"; }
+
+.fa-sticker-mule:before {
+  content: "\f3f7"; }
+
+.fa-creative-commons-zero:before {
+  content: "\f4f3"; }
+
+.fa-hips:before {
+  content: "\f452"; }
+
+.fa-behance:before {
+  content: "\f1b4"; }
+
+.fa-reddit:before {
+  content: "\f1a1"; }
+
+.fa-discord:before {
+  content: "\f392"; }
+
+.fa-chrome:before {
+  content: "\f268"; }
+
+.fa-app-store-ios:before {
+  content: "\f370"; }
+
+.fa-cc-discover:before {
+  content: "\f1f2"; }
+
+.fa-wpbeginner:before {
+  content: "\f297"; }
+
+.fa-confluence:before {
+  content: "\f78d"; }
+
+.fa-mdb:before {
+  content: "\f8ca"; }
+
+.fa-dochub:before {
+  content: "\f394"; }
+
+.fa-accessible-icon:before {
+  content: "\f368"; }
+
+.fa-ebay:before {
+  content: "\f4f4"; }
+
+.fa-amazon:before {
+  content: "\f270"; }
+
+.fa-unsplash:before {
+  content: "\e07c"; }
+
+.fa-yarn:before {
+  content: "\f7e3"; }
+
+.fa-square-steam:before {
+  content: "\f1b7"; }
+
+.fa-steam-square:before {
+  content: "\f1b7"; }
+
+.fa-500px:before {
+  content: "\f26e"; }
+
+.fa-square-vimeo:before {
+  content: "\f194"; }
+
+.fa-vimeo-square:before {
+  content: "\f194"; }
+
+.fa-asymmetrik:before {
+  content: "\f372"; }
+
+.fa-font-awesome:before {
+  content: "\f2b4"; }
+
+.fa-font-awesome-flag:before {
+  content: "\f2b4"; }
+
+.fa-font-awesome-logo-full:before {
+  content: "\f2b4"; }
+
+.fa-gratipay:before {
+  content: "\f184"; }
+
+.fa-apple:before {
+  content: "\f179"; }
+
+.fa-hive:before {
+  content: "\e07f"; }
+
+.fa-gitkraken:before {
+  content: "\f3a6"; }
+
+.fa-keybase:before {
+  content: "\f4f5"; }
+
+.fa-apple-pay:before {
+  content: "\f415"; }
+
+.fa-padlet:before {
+  content: "\e4a0"; }
+
+.fa-amazon-pay:before {
+  content: "\f42c"; }
+
+.fa-square-github:before {
+  content: "\f092"; }
+
+.fa-github-square:before {
+  content: "\f092"; }
+
+.fa-stumbleupon:before {
+  content: "\f1a4"; }
+
+.fa-fedex:before {
+  content: "\f797"; }
+
+.fa-phoenix-framework:before {
+  content: "\f3dc"; }
+
+.fa-shopify:before {
+  content: "\e057"; }
+
+.fa-neos:before {
+  content: "\f612"; }
+
+.fa-square-threads:before {
+  content: "\e619"; }
+
+.fa-hackerrank:before {
+  content: "\f5f7"; }
+
+.fa-researchgate:before {
+  content: "\f4f8"; }
+
+.fa-swift:before {
+  content: "\f8e1"; }
+
+.fa-angular:before {
+  content: "\f420"; }
+
+.fa-speakap:before {
+  content: "\f3f3"; }
+
+.fa-angrycreative:before {
+  content: "\f36e"; }
+
+.fa-y-combinator:before {
+  content: "\f23b"; }
+
+.fa-empire:before {
+  content: "\f1d1"; }
+
+.fa-envira:before {
+  content: "\f299"; }
+
+.fa-square-gitlab:before {
+  content: "\e5ae"; }
+
+.fa-gitlab-square:before {
+  content: "\e5ae"; }
+
+.fa-studiovinari:before {
+  content: "\f3f8"; }
+
+.fa-pied-piper:before {
+  content: "\f2ae"; }
+
+.fa-wordpress:before {
+  content: "\f19a"; }
+
+.fa-product-hunt:before {
+  content: "\f288"; }
+
+.fa-firefox:before {
+  content: "\f269"; }
+
+.fa-linode:before {
+  content: "\f2b8"; }
+
+.fa-goodreads:before {
+  content: "\f3a8"; }
+
+.fa-square-odnoklassniki:before {
+  content: "\f264"; }
+
+.fa-odnoklassniki-square:before {
+  content: "\f264"; }
+
+.fa-jsfiddle:before {
+  content: "\f1cc"; }
+
+.fa-sith:before {
+  content: "\f512"; }
+
+.fa-themeisle:before {
+  content: "\f2b2"; }
+
+.fa-page4:before {
+  content: "\f3d7"; }
+
+.fa-hashnode:before {
+  content: "\e499"; }
+
+.fa-react:before {
+  content: "\f41b"; }
+
+.fa-cc-paypal:before {
+  content: "\f1f4"; }
+
+.fa-squarespace:before {
+  content: "\f5be"; }
+
+.fa-cc-stripe:before {
+  content: "\f1f5"; }
+
+.fa-creative-commons-share:before {
+  content: "\f4f2"; }
+
+.fa-bitcoin:before {
+  content: "\f379"; }
+
+.fa-keycdn:before {
+  content: "\f3ba"; }
+
+.fa-opera:before {
+  content: "\f26a"; }
+
+.fa-itch-io:before {
+  content: "\f83a"; }
+
+.fa-umbraco:before {
+  content: "\f8e8"; }
+
+.fa-galactic-senate:before {
+  content: "\f50d"; }
+
+.fa-ubuntu:before {
+  content: "\f7df"; }
+
+.fa-draft2digital:before {
+  content: "\f396"; }
+
+.fa-stripe:before {
+  content: "\f429"; }
+
+.fa-houzz:before {
+  content: "\f27c"; }
+
+.fa-gg:before {
+  content: "\f260"; }
+
+.fa-dhl:before {
+  content: "\f790"; }
+
+.fa-square-pinterest:before {
+  content: "\f0d3"; }
+
+.fa-pinterest-square:before {
+  content: "\f0d3"; }
+
+.fa-xing:before {
+  content: "\f168"; }
+
+.fa-blackberry:before {
+  content: "\f37b"; }
+
+.fa-creative-commons-pd:before {
+  content: "\f4ec"; }
+
+.fa-playstation:before {
+  content: "\f3df"; }
+
+.fa-quinscape:before {
+  content: "\f459"; }
+
+.fa-less:before {
+  content: "\f41d"; }
+
+.fa-blogger-b:before {
+  content: "\f37d"; }
+
+.fa-opencart:before {
+  content: "\f23d"; }
+
+.fa-vine:before {
+  content: "\f1ca"; }
+
+.fa-paypal:before {
+  content: "\f1ed"; }
+
+.fa-gitlab:before {
+  content: "\f296"; }
+
+.fa-typo3:before {
+  content: "\f42b"; }
+
+.fa-reddit-alien:before {
+  content: "\f281"; }
+
+.fa-yahoo:before {
+  content: "\f19e"; }
+
+.fa-dailymotion:before {
+  content: "\e052"; }
+
+.fa-affiliatetheme:before {
+  content: "\f36b"; }
+
+.fa-pied-piper-pp:before {
+  content: "\f1a7"; }
+
+.fa-bootstrap:before {
+  content: "\f836"; }
+
+.fa-odnoklassniki:before {
+  content: "\f263"; }
+
+.fa-nfc-symbol:before {
+  content: "\e531"; }
+
+.fa-ethereum:before {
+  content: "\f42e"; }
+
+.fa-speaker-deck:before {
+  content: "\f83c"; }
+
+.fa-creative-commons-nc-eu:before {
+  content: "\f4e9"; }
+
+.fa-patreon:before {
+  content: "\f3d9"; }
+
+.fa-avianex:before {
+  content: "\f374"; }
+
+.fa-ello:before {
+  content: "\f5f1"; }
+
+.fa-gofore:before {
+  content: "\f3a7"; }
+
+.fa-bimobject:before {
+  content: "\f378"; }
+
+.fa-facebook-f:before {
+  content: "\f39e"; }
+
+.fa-square-google-plus:before {
+  content: "\f0d4"; }
+
+.fa-google-plus-square:before {
+  content: "\f0d4"; }
+
+.fa-mandalorian:before {
+  content: "\f50f"; }
+
+.fa-first-order-alt:before {
+  content: "\f50a"; }
+
+.fa-osi:before {
+  content: "\f41a"; }
+
+.fa-google-wallet:before {
+  content: "\f1ee"; }
+
+.fa-d-and-d-beyond:before {
+  content: "\f6ca"; }
+
+.fa-periscope:before {
+  content: "\f3da"; }
+
+.fa-fulcrum:before {
+  content: "\f50b"; }
+
+.fa-cloudscale:before {
+  content: "\f383"; }
+
+.fa-forumbee:before {
+  content: "\f211"; }
+
+.fa-mizuni:before {
+  content: "\f3cc"; }
+
+.fa-schlix:before {
+  content: "\f3ea"; }
+
+.fa-square-xing:before {
+  content: "\f169"; }
+
+.fa-xing-square:before {
+  content: "\f169"; }
+
+.fa-bandcamp:before {
+  content: "\f2d5"; }
+
+.fa-wpforms:before {
+  content: "\f298"; }
+
+.fa-cloudversify:before {
+  content: "\f385"; }
+
+.fa-usps:before {
+  content: "\f7e1"; }
+
+.fa-megaport:before {
+  content: "\f5a3"; }
+
+.fa-magento:before {
+  content: "\f3c4"; }
+
+.fa-spotify:before {
+  content: "\f1bc"; }
+
+.fa-optin-monster:before {
+  content: "\f23c"; }
+
+.fa-fly:before {
+  content: "\f417"; }
+
+.fa-aviato:before {
+  content: "\f421"; }
+
+.fa-itunes:before {
+  content: "\f3b4"; }
+
+.fa-cuttlefish:before {
+  content: "\f38c"; }
+
+.fa-blogger:before {
+  content: "\f37c"; }
+
+.fa-flickr:before {
+  content: "\f16e"; }
+
+.fa-viber:before {
+  content: "\f409"; }
+
+.fa-soundcloud:before {
+  content: "\f1be"; }
+
+.fa-digg:before {
+  content: "\f1a6"; }
+
+.fa-tencent-weibo:before {
+  content: "\f1d5"; }
+
+.fa-symfony:before {
+  content: "\f83d"; }
+
+.fa-maxcdn:before {
+  content: "\f136"; }
+
+.fa-etsy:before {
+  content: "\f2d7"; }
+
+.fa-facebook-messenger:before {
+  content: "\f39f"; }
+
+.fa-audible:before {
+  content: "\f373"; }
+
+.fa-think-peaks:before {
+  content: "\f731"; }
+
+.fa-bilibili:before {
+  content: "\e3d9"; }
+
+.fa-erlang:before {
+  content: "\f39d"; }
+
+.fa-x-twitter:before {
+  content: "\e61b"; }
+
+.fa-cotton-bureau:before {
+  content: "\f89e"; }
+
+.fa-dashcube:before {
+  content: "\f210"; }
+
+.fa-42-group:before {
+  content: "\e080"; }
+
+.fa-innosoft:before {
+  content: "\e080"; }
+
+.fa-stack-exchange:before {
+  content: "\f18d"; }
+
+.fa-elementor:before {
+  content: "\f430"; }
+
+.fa-square-pied-piper:before {
+  content: "\e01e"; }
+
+.fa-pied-piper-square:before {
+  content: "\e01e"; }
+
+.fa-creative-commons-nd:before {
+  content: "\f4eb"; }
+
+.fa-palfed:before {
+  content: "\f3d8"; }
+
+.fa-superpowers:before {
+  content: "\f2dd"; }
+
+.fa-resolving:before {
+  content: "\f3e7"; }
+
+.fa-xbox:before {
+  content: "\f412"; }
+
+.fa-searchengin:before {
+  content: "\f3eb"; }
+
+.fa-tiktok:before {
+  content: "\e07b"; }
+
+.fa-square-facebook:before {
+  content: "\f082"; }
+
+.fa-facebook-square:before {
+  content: "\f082"; }
+
+.fa-renren:before {
+  content: "\f18b"; }
+
+.fa-linux:before {
+  content: "\f17c"; }
+
+.fa-glide:before {
+  content: "\f2a5"; }
+
+.fa-linkedin:before {
+  content: "\f08c"; }
+
+.fa-hubspot:before {
+  content: "\f3b2"; }
+
+.fa-deploydog:before {
+  content: "\f38e"; }
+
+.fa-twitch:before {
+  content: "\f1e8"; }
+
+.fa-ravelry:before {
+  content: "\f2d9"; }
+
+.fa-mixer:before {
+  content: "\e056"; }
+
+.fa-square-lastfm:before {
+  content: "\f203"; }
+
+.fa-lastfm-square:before {
+  content: "\f203"; }
+
+.fa-vimeo:before {
+  content: "\f40a"; }
+
+.fa-mendeley:before {
+  content: "\f7b3"; }
+
+.fa-uniregistry:before {
+  content: "\f404"; }
+
+.fa-figma:before {
+  content: "\f799"; }
+
+.fa-creative-commons-remix:before {
+  content: "\f4ee"; }
+
+.fa-cc-amazon-pay:before {
+  content: "\f42d"; }
+
+.fa-dropbox:before {
+  content: "\f16b"; }
+
+.fa-instagram:before {
+  content: "\f16d"; }
+
+.fa-cmplid:before {
+  content: "\e360"; }
+
+.fa-facebook:before {
+  content: "\f09a"; }
+
+.fa-gripfire:before {
+  content: "\f3ac"; }
+
+.fa-jedi-order:before {
+  content: "\f50e"; }
+
+.fa-uikit:before {
+  content: "\f403"; }
+
+.fa-fort-awesome-alt:before {
+  content: "\f3a3"; }
+
+.fa-phabricator:before {
+  content: "\f3db"; }
+
+.fa-ussunnah:before {
+  content: "\f407"; }
+
+.fa-earlybirds:before {
+  content: "\f39a"; }
+
+.fa-trade-federation:before {
+  content: "\f513"; }
+
+.fa-autoprefixer:before {
+  content: "\f41c"; }
+
+.fa-whatsapp:before {
+  content: "\f232"; }
+
+.fa-slideshare:before {
+  content: "\f1e7"; }
+
+.fa-google-play:before {
+  content: "\f3ab"; }
+
+.fa-viadeo:before {
+  content: "\f2a9"; }
+
+.fa-line:before {
+  content: "\f3c0"; }
+
+.fa-google-drive:before {
+  content: "\f3aa"; }
+
+.fa-servicestack:before {
+  content: "\f3ec"; }
+
+.fa-simplybuilt:before {
+  content: "\f215"; }
+
+.fa-bitbucket:before {
+  content: "\f171"; }
+
+.fa-imdb:before {
+  content: "\f2d8"; }
+
+.fa-deezer:before {
+  content: "\e077"; }
+
+.fa-raspberry-pi:before {
+  content: "\f7bb"; }
+
+.fa-jira:before {
+  content: "\f7b1"; }
+
+.fa-docker:before {
+  content: "\f395"; }
+
+.fa-screenpal:before {
+  content: "\e570"; }
+
+.fa-bluetooth:before {
+  content: "\f293"; }
+
+.fa-gitter:before {
+  content: "\f426"; }
+
+.fa-d-and-d:before {
+  content: "\f38d"; }
+
+.fa-microblog:before {
+  content: "\e01a"; }
+
+.fa-cc-diners-club:before {
+  content: "\f24c"; }
+
+.fa-gg-circle:before {
+  content: "\f261"; }
+
+.fa-pied-piper-hat:before {
+  content: "\f4e5"; }
+
+.fa-kickstarter-k:before {
+  content: "\f3bc"; }
+
+.fa-yandex:before {
+  content: "\f413"; }
+
+.fa-readme:before {
+  content: "\f4d5"; }
+
+.fa-html5:before {
+  content: "\f13b"; }
+
+.fa-sellsy:before {
+  content: "\f213"; }
+
+.fa-sass:before {
+  content: "\f41e"; }
+
+.fa-wirsindhandwerk:before {
+  content: "\e2d0"; }
+
+.fa-wsh:before {
+  content: "\e2d0"; }
+
+.fa-buromobelexperte:before {
+  content: "\f37f"; }
+
+.fa-salesforce:before {
+  content: "\f83b"; }
+
+.fa-octopus-deploy:before {
+  content: "\e082"; }
+
+.fa-medapps:before {
+  content: "\f3c6"; }
+
+.fa-ns8:before {
+  content: "\f3d5"; }
+
+.fa-pinterest-p:before {
+  content: "\f231"; }
+
+.fa-apper:before {
+  content: "\f371"; }
+
+.fa-fort-awesome:before {
+  content: "\f286"; }
+
+.fa-waze:before {
+  content: "\f83f"; }
+
+.fa-cc-jcb:before {
+  content: "\f24b"; }
+
+.fa-snapchat:before {
+  content: "\f2ab"; }
+
+.fa-snapchat-ghost:before {
+  content: "\f2ab"; }
+
+.fa-fantasy-flight-games:before {
+  content: "\f6dc"; }
+
+.fa-rust:before {
+  content: "\e07a"; }
+
+.fa-wix:before {
+  content: "\f5cf"; }
+
+.fa-square-behance:before {
+  content: "\f1b5"; }
+
+.fa-behance-square:before {
+  content: "\f1b5"; }
+
+.fa-supple:before {
+  content: "\f3f9"; }
+
+.fa-rebel:before {
+  content: "\f1d0"; }
+
+.fa-css3:before {
+  content: "\f13c"; }
+
+.fa-staylinked:before {
+  content: "\f3f5"; }
+
+.fa-kaggle:before {
+  content: "\f5fa"; }
+
+.fa-space-awesome:before {
+  content: "\e5ac"; }
+
+.fa-deviantart:before {
+  content: "\f1bd"; }
+
+.fa-cpanel:before {
+  content: "\f388"; }
+
+.fa-goodreads-g:before {
+  content: "\f3a9"; }
+
+.fa-square-git:before {
+  content: "\f1d2"; }
+
+.fa-git-square:before {
+  content: "\f1d2"; }
+
+.fa-square-tumblr:before {
+  content: "\f174"; }
+
+.fa-tumblr-square:before {
+  content: "\f174"; }
+
+.fa-trello:before {
+  content: "\f181"; }
+
+.fa-creative-commons-nc-jp:before {
+  content: "\f4ea"; }
+
+.fa-get-pocket:before {
+  content: "\f265"; }
+
+.fa-perbyte:before {
+  content: "\e083"; }
+
+.fa-grunt:before {
+  content: "\f3ad"; }
+
+.fa-weebly:before {
+  content: "\f5cc"; }
+
+.fa-connectdevelop:before {
+  content: "\f20e"; }
+
+.fa-leanpub:before {
+  content: "\f212"; }
+
+.fa-black-tie:before {
+  content: "\f27e"; }
+
+.fa-themeco:before {
+  content: "\f5c6"; }
+
+.fa-python:before {
+  content: "\f3e2"; }
+
+.fa-android:before {
+  content: "\f17b"; }
+
+.fa-bots:before {
+  content: "\e340"; }
+
+.fa-free-code-camp:before {
+  content: "\f2c5"; }
+
+.fa-hornbill:before {
+  content: "\f592"; }
+
+.fa-js:before {
+  content: "\f3b8"; }
+
+.fa-ideal:before {
+  content: "\e013"; }
+
+.fa-git:before {
+  content: "\f1d3"; }
+
+.fa-dev:before {
+  content: "\f6cc"; }
+
+.fa-sketch:before {
+  content: "\f7c6"; }
+
+.fa-yandex-international:before {
+  content: "\f414"; }
+
+.fa-cc-amex:before {
+  content: "\f1f3"; }
+
+.fa-uber:before {
+  content: "\f402"; }
+
+.fa-github:before {
+  content: "\f09b"; }
+
+.fa-php:before {
+  content: "\f457"; }
+
+.fa-alipay:before {
+  content: "\f642"; }
+
+.fa-youtube:before {
+  content: "\f167"; }
+
+.fa-skyatlas:before {
+  content: "\f216"; }
+
+.fa-firefox-browser:before {
+  content: "\e007"; }
+
+.fa-replyd:before {
+  content: "\f3e6"; }
+
+.fa-suse:before {
+  content: "\f7d6"; }
+
+.fa-jenkins:before {
+  content: "\f3b6"; }
+
+.fa-twitter:before {
+  content: "\f099"; }
+
+.fa-rockrms:before {
+  content: "\f3e9"; }
+
+.fa-pinterest:before {
+  content: "\f0d2"; }
+
+.fa-buffer:before {
+  content: "\f837"; }
+
+.fa-npm:before {
+  content: "\f3d4"; }
+
+.fa-yammer:before {
+  content: "\f840"; }
+
+.fa-btc:before {
+  content: "\f15a"; }
+
+.fa-dribbble:before {
+  content: "\f17d"; }
+
+.fa-stumbleupon-circle:before {
+  content: "\f1a3"; }
+
+.fa-internet-explorer:before {
+  content: "\f26b"; }
+
+.fa-stubber:before {
+  content: "\e5c7"; }
+
+.fa-telegram:before {
+  content: "\f2c6"; }
+
+.fa-telegram-plane:before {
+  content: "\f2c6"; }
+
+.fa-old-republic:before {
+  content: "\f510"; }
+
+.fa-odysee:before {
+  content: "\e5c6"; }
+
+.fa-square-whatsapp:before {
+  content: "\f40c"; }
+
+.fa-whatsapp-square:before {
+  content: "\f40c"; }
+
+.fa-node-js:before {
+  content: "\f3d3"; }
+
+.fa-edge-legacy:before {
+  content: "\e078"; }
+
+.fa-slack:before {
+  content: "\f198"; }
+
+.fa-slack-hash:before {
+  content: "\f198"; }
+
+.fa-medrt:before {
+  content: "\f3c8"; }
+
+.fa-usb:before {
+  content: "\f287"; }
+
+.fa-tumblr:before {
+  content: "\f173"; }
+
+.fa-vaadin:before {
+  content: "\f408"; }
+
+.fa-quora:before {
+  content: "\f2c4"; }
+
+.fa-square-x-twitter:before {
+  content: "\e61a"; }
+
+.fa-reacteurope:before {
+  content: "\f75d"; }
+
+.fa-medium:before {
+  content: "\f23a"; }
+
+.fa-medium-m:before {
+  content: "\f23a"; }
+
+.fa-amilia:before {
+  content: "\f36d"; }
+
+.fa-mixcloud:before {
+  content: "\f289"; }
+
+.fa-flipboard:before {
+  content: "\f44d"; }
+
+.fa-viacoin:before {
+  content: "\f237"; }
+
+.fa-critical-role:before {
+  content: "\f6c9"; }
+
+.fa-sitrox:before {
+  content: "\e44a"; }
+
+.fa-discourse:before {
+  content: "\f393"; }
+
+.fa-joomla:before {
+  content: "\f1aa"; }
+
+.fa-mastodon:before {
+  content: "\f4f6"; }
+
+.fa-airbnb:before {
+  content: "\f834"; }
+
+.fa-wolf-pack-battalion:before {
+  content: "\f514"; }
+
+.fa-buy-n-large:before {
+  content: "\f8a6"; }
+
+.fa-gulp:before {
+  content: "\f3ae"; }
+
+.fa-creative-commons-sampling-plus:before {
+  content: "\f4f1"; }
+
+.fa-strava:before {
+  content: "\f428"; }
+
+.fa-ember:before {
+  content: "\f423"; }
+
+.fa-canadian-maple-leaf:before {
+  content: "\f785"; }
+
+.fa-teamspeak:before {
+  content: "\f4f9"; }
+
+.fa-pushed:before {
+  content: "\f3e1"; }
+
+.fa-wordpress-simple:before {
+  content: "\f411"; }
+
+.fa-nutritionix:before {
+  content: "\f3d6"; }
+
+.fa-wodu:before {
+  content: "\e088"; }
+
+.fa-google-pay:before {
+  content: "\e079"; }
+
+.fa-intercom:before {
+  content: "\f7af"; }
+
+.fa-zhihu:before {
+  content: "\f63f"; }
+
+.fa-korvue:before {
+  content: "\f42f"; }
+
+.fa-pix:before {
+  content: "\e43a"; }
+
+.fa-steam-symbol:before {
+  content: "\f3f6"; }
+:root, :host {
+  --fa-style-family-classic: 'Font Awesome 6 Free';
+  --fa-font-regular: normal 400 1em/1 'Font Awesome 6 Free'; }
+
+@font-face {
+  font-family: 'Font Awesome 6 Free';
+  font-style: normal;
+  font-weight: 400;
+  font-display: block;
+  src: url("../webfonts/fa-regular-400.woff2") format("woff2"), url("../webfonts/fa-regular-400.ttf") format("truetype"); }
+
+.far,
+.fa-regular {
+  font-weight: 400; }
+:root, :host {
+  --fa-style-family-classic: 'Font Awesome 6 Free';
+  --fa-font-solid: normal 900 1em/1 'Font Awesome 6 Free'; }
+
+@font-face {
+  font-family: 'Font Awesome 6 Free';
+  font-style: normal;
+  font-weight: 900;
+  font-display: block;
+  src: url("../webfonts/fa-solid-900.woff2") format("woff2"), url("../webfonts/fa-solid-900.ttf") format("truetype"); }
+
+.fas,
+.fa-solid {
+  font-weight: 900; }
+@font-face {
+  font-family: 'Font Awesome 5 Brands';
+  font-display: block;
+  font-weight: 400;
+  src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); }
+
+@font-face {
+  font-family: 'Font Awesome 5 Free';
+  font-display: block;
+  font-weight: 900;
+  src: url("../webfonts/fa-solid-900.woff2") format("woff2"), url("../webfonts/fa-solid-900.ttf") format("truetype"); }
+
+@font-face {
+  font-family: 'Font Awesome 5 Free';
+  font-display: block;
+  font-weight: 400;
+  src: url("../webfonts/fa-regular-400.woff2") format("woff2"), url("../webfonts/fa-regular-400.ttf") format("truetype"); }
+@font-face {
+  font-family: 'FontAwesome';
+  font-display: block;
+  src: url("../webfonts/fa-solid-900.woff2") format("woff2"), url("../webfonts/fa-solid-900.ttf") format("truetype"); }
+
+@font-face {
+  font-family: 'FontAwesome';
+  font-display: block;
+  src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); }
+
+@font-face {
+  font-family: 'FontAwesome';
+  font-display: block;
+  src: url("../webfonts/fa-regular-400.woff2") format("woff2"), url("../webfonts/fa-regular-400.ttf") format("truetype"); }
+
+@font-face {
+  font-family: 'FontAwesome';
+  font-display: block;
+  src: url("../webfonts/fa-v4compatibility.woff2") format("woff2"), url("../webfonts/fa-v4compatibility.ttf") format("truetype"); }
diff --git a/deps/font-awesome-6.4.2/css/all.min.css b/deps/font-awesome-6.4.2/css/all.min.css
new file mode 100644
index 0000000..6604a06
--- /dev/null
+++ b/deps/font-awesome-6.4.2/css/all.min.css
@@ -0,0 +1,9 @@
+/*!
+ * Font Awesome Free 6.4.2 by @fontawesome - https://fontawesome.com
+ * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
+ * Copyright 2023 Fonticons, Inc.
+ */
+.fa{font-family:var(--fa-style-family,"Font Awesome 6 Free");font-weight:var(--fa-style,900)}.fa,.fa-brands,.fa-classic,.fa-regular,.fa-sharp,.fa-solid,.fab,.far,.fas{-moz-osx-font-smoothing:grayscale;-webkit-font-smoothing:antialiased;display:var(--fa-display,inline-block);font-style:normal;font-variant:normal;line-height:1;text-rendering:auto}.fa-classic,.fa-regular,.fa-solid,.far,.fas{font-family:"Font Awesome 6 Free"}.fa-brands,.fab{font-family:"Font Awesome 6 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diff --git a/deps/font-awesome-6.4.2/css/v4-shims.css b/deps/font-awesome-6.4.2/css/v4-shims.css
new file mode 100644
index 0000000..a85953d
--- /dev/null
+++ b/deps/font-awesome-6.4.2/css/v4-shims.css
@@ -0,0 +1,2194 @@
+/*!
+ * Font Awesome Free 6.4.2 by @fontawesome - https://fontawesome.com
+ * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
+ * Copyright 2023 Fonticons, Inc.
+ */
+.fa.fa-glass:before {
+  content: "\f000"; }
+
+.fa.fa-envelope-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-envelope-o:before {
+  content: "\f0e0"; }
+
+.fa.fa-star-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-star-o:before {
+  content: "\f005"; }
+
+.fa.fa-remove:before {
+  content: "\f00d"; }
+
+.fa.fa-close:before {
+  content: "\f00d"; }
+
+.fa.fa-gear:before {
+  content: "\f013"; }
+
+.fa.fa-trash-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-trash-o:before {
+  content: "\f2ed"; }
+
+.fa.fa-home:before {
+  content: "\f015"; }
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+.fa.fa-file-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+.fa.fa-file-o:before {
+  content: "\f15b"; }
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+.fa.fa-clock-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+.fa.fa-clock-o:before {
+  content: "\f017"; }
+
+.fa.fa-arrow-circle-o-down {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+.fa.fa-arrow-circle-o-down:before {
+  content: "\f358"; }
+
+.fa.fa-arrow-circle-o-up {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-arrow-circle-o-up:before {
+  content: "\f35b"; }
+
+.fa.fa-play-circle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-play-circle-o:before {
+  content: "\f144"; }
+
+.fa.fa-repeat:before {
+  content: "\f01e"; }
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+.fa.fa-rotate-right:before {
+  content: "\f01e"; }
+
+.fa.fa-refresh:before {
+  content: "\f021"; }
+
+.fa.fa-list-alt {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-list-alt:before {
+  content: "\f022"; }
+
+.fa.fa-dedent:before {
+  content: "\f03b"; }
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+.fa.fa-video-camera:before {
+  content: "\f03d"; }
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+.fa.fa-picture-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+  content: "\f03e"; }
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+.fa.fa-photo {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+.fa.fa-photo:before {
+  content: "\f03e"; }
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+.fa.fa-image {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+.fa.fa-image:before {
+  content: "\f03e"; }
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+.fa.fa-map-marker:before {
+  content: "\f3c5"; }
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+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+.fa.fa-pencil-square-o:before {
+  content: "\f044"; }
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+.fa.fa-edit {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+.fa.fa-edit:before {
+  content: "\f044"; }
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+.fa.fa-share-square-o:before {
+  content: "\f14d"; }
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+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+.fa.fa-check-square-o:before {
+  content: "\f14a"; }
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+.fa.fa-arrows:before {
+  content: "\f0b2"; }
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+.fa.fa-times-circle-o {
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+  font-weight: 400; }
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+.fa.fa-times-circle-o:before {
+  content: "\f057"; }
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+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+.fa.fa-check-circle-o:before {
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+.fa.fa-compress:before {
+  content: "\f422"; }
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+.fa.fa-eye {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+.fa.fa-eye-slash {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+.fa.fa-warning:before {
+  content: "\f071"; }
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+.fa.fa-calendar:before {
+  content: "\f073"; }
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+  content: "\f338"; }
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+  content: "\f337"; }
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+.fa.fa-bar-chart:before {
+  content: "\e0e3"; }
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+.fa.fa-bar-chart-o:before {
+  content: "\e0e3"; }
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+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
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+  font-weight: 400; }
+
+.fa.fa-pause-circle-o:before {
+  content: "\f28b"; }
+
+.fa.fa-stop-circle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-stop-circle-o:before {
+  content: "\f28d"; }
+
+.fa.fa-bluetooth {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-bluetooth-b {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-gitlab {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-wpbeginner {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-wpforms {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-envira {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-wheelchair-alt {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-wheelchair-alt:before {
+  content: "\f368"; }
+
+.fa.fa-question-circle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-question-circle-o:before {
+  content: "\f059"; }
+
+.fa.fa-volume-control-phone:before {
+  content: "\f2a0"; }
+
+.fa.fa-asl-interpreting:before {
+  content: "\f2a3"; }
+
+.fa.fa-deafness:before {
+  content: "\f2a4"; }
+
+.fa.fa-hard-of-hearing:before {
+  content: "\f2a4"; }
+
+.fa.fa-glide {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-glide-g {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-signing:before {
+  content: "\f2a7"; }
+
+.fa.fa-viadeo {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-viadeo-square {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-viadeo-square:before {
+  content: "\f2aa"; }
+
+.fa.fa-snapchat {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-snapchat-ghost {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-snapchat-ghost:before {
+  content: "\f2ab"; }
+
+.fa.fa-snapchat-square {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-snapchat-square:before {
+  content: "\f2ad"; }
+
+.fa.fa-pied-piper {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-first-order {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-yoast {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-themeisle {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-google-plus-official {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-google-plus-official:before {
+  content: "\f2b3"; }
+
+.fa.fa-google-plus-circle {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-google-plus-circle:before {
+  content: "\f2b3"; }
+
+.fa.fa-font-awesome {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-fa {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-fa:before {
+  content: "\f2b4"; }
+
+.fa.fa-handshake-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-handshake-o:before {
+  content: "\f2b5"; }
+
+.fa.fa-envelope-open-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-envelope-open-o:before {
+  content: "\f2b6"; }
+
+.fa.fa-linode {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-address-book-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-address-book-o:before {
+  content: "\f2b9"; }
+
+.fa.fa-vcard:before {
+  content: "\f2bb"; }
+
+.fa.fa-address-card-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-address-card-o:before {
+  content: "\f2bb"; }
+
+.fa.fa-vcard-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-vcard-o:before {
+  content: "\f2bb"; }
+
+.fa.fa-user-circle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-user-circle-o:before {
+  content: "\f2bd"; }
+
+.fa.fa-user-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-user-o:before {
+  content: "\f007"; }
+
+.fa.fa-id-badge {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-drivers-license:before {
+  content: "\f2c2"; }
+
+.fa.fa-id-card-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-id-card-o:before {
+  content: "\f2c2"; }
+
+.fa.fa-drivers-license-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-drivers-license-o:before {
+  content: "\f2c2"; }
+
+.fa.fa-quora {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-free-code-camp {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-telegram {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-thermometer-4:before {
+  content: "\f2c7"; }
+
+.fa.fa-thermometer:before {
+  content: "\f2c7"; }
+
+.fa.fa-thermometer-3:before {
+  content: "\f2c8"; }
+
+.fa.fa-thermometer-2:before {
+  content: "\f2c9"; }
+
+.fa.fa-thermometer-1:before {
+  content: "\f2ca"; }
+
+.fa.fa-thermometer-0:before {
+  content: "\f2cb"; }
+
+.fa.fa-bathtub:before {
+  content: "\f2cd"; }
+
+.fa.fa-s15:before {
+  content: "\f2cd"; }
+
+.fa.fa-window-maximize {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-window-restore {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-times-rectangle:before {
+  content: "\f410"; }
+
+.fa.fa-window-close-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-window-close-o:before {
+  content: "\f410"; }
+
+.fa.fa-times-rectangle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-times-rectangle-o:before {
+  content: "\f410"; }
+
+.fa.fa-bandcamp {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-grav {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-etsy {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-imdb {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-ravelry {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-eercast {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-eercast:before {
+  content: "\f2da"; }
+
+.fa.fa-snowflake-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-snowflake-o:before {
+  content: "\f2dc"; }
+
+.fa.fa-superpowers {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-wpexplorer {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-meetup {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
diff --git a/deps/font-awesome-6.4.2/css/v4-shims.min.css b/deps/font-awesome-6.4.2/css/v4-shims.min.css
new file mode 100644
index 0000000..64e4e8d
--- /dev/null
+++ b/deps/font-awesome-6.4.2/css/v4-shims.min.css
@@ -0,0 +1,6 @@
+/*!
+ * Font Awesome Free 6.4.2 by @fontawesome - https://fontawesome.com
+ * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
+ * Copyright 2023 Fonticons, Inc.
+ */
+.fa.fa-glass:before{content:"\f000"}.fa.fa-envelope-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-envelope-o:before{content:"\f0e0"}.fa.fa-star-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-star-o:before{content:"\f005"}.fa.fa-close:before,.fa.fa-remove:before{content:"\f00d"}.fa.fa-gear:before{content:"\f013"}.fa.fa-trash-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-trash-o:before{content:"\f2ed"}.fa.fa-home:before{content:"\f015"}.fa.fa-file-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-o:before{content:"\f15b"}.fa.fa-clock-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-clock-o:before{content:"\f017"}.fa.fa-arrow-circle-o-down{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-arrow-circle-o-down:before{content:"\f358"}.fa.fa-arrow-circle-o-up{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-arrow-circle-o-up:before{content:"\f35b"}.fa.fa-play-circle-o{font-family:"Font Awesome 6 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+/*!
+ * headroom.js v0.11.0 - Give your page some headroom. Hide your header until you need it
+ * Copyright (c) 2020 Nick Williams - http://wicky.nillia.ms/headroom.js
+ * License: MIT
+ */
+
+!function(t,n){"object"==typeof exports&&"undefined"!=typeof module?module.exports=n():"function"==typeof define&&define.amd?define(n):(t=t||self).Headroom=n()}(this,function(){"use strict";function t(){return"undefined"!=typeof window}function d(t){return function(t){return t&&t.document&&function(t){return 9===t.nodeType}(t.document)}(t)?function(t){var n=t.document,o=n.body,s=n.documentElement;return{scrollHeight:function(){return Math.max(o.scrollHeight,s.scrollHeight,o.offsetHeight,s.offsetHeight,o.clientHeight,s.clientHeight)},height:function(){return t.innerHeight||s.clientHeight||o.clientHeight},scrollY:function(){return void 0!==t.pageYOffset?t.pageYOffset:(s||o.parentNode||o).scrollTop}}}(t):function(t){return{scrollHeight:function(){return Math.max(t.scrollHeight,t.offsetHeight,t.clientHeight)},height:function(){return Math.max(t.offsetHeight,t.clientHeight)},scrollY:function(){return t.scrollTop}}}(t)}function n(t,s,e){var n,o=function(){var n=!1;try{var t={get passive(){n=!0}};window.addEventListener("test",t,t),window.removeEventListener("test",t,t)}catch(t){n=!1}return n}(),i=!1,r=d(t),l=r.scrollY(),a={};function c(){var t=Math.round(r.scrollY()),n=r.height(),o=r.scrollHeight();a.scrollY=t,a.lastScrollY=l,a.direction=l<t?"down":"up",a.distance=Math.abs(t-l),a.isOutOfBounds=t<0||o<t+n,a.top=t<=s.offset,a.bottom=o<=t+n,a.toleranceExceeded=a.distance>s.tolerance[a.direction],e(a),l=t,i=!1}function h(){i||(i=!0,n=requestAnimationFrame(c))}var u=!!o&&{passive:!0,capture:!1};return t.addEventListener("scroll",h,u),c(),{destroy:function(){cancelAnimationFrame(n),t.removeEventListener("scroll",h,u)}}}function o(t,n){n=n||{},Object.assign(this,o.options,n),this.classes=Object.assign({},o.options.classes,n.classes),this.elem=t,this.tolerance=function(t){return t===Object(t)?t:{down:t,up:t}}(this.tolerance),this.initialised=!1,this.frozen=!1}return o.prototype={constructor:o,init:function(){return o.cutsTheMustard&&!this.initialised&&(this.addClass("initial"),this.initialised=!0,setTimeout(function(t){t.scrollTracker=n(t.scroller,{offset:t.offset,tolerance:t.tolerance},t.update.bind(t))},100,this)),this},destroy:function(){this.initialised=!1,Object.keys(this.classes).forEach(this.removeClass,this),this.scrollTracker.destroy()},unpin:function(){!this.hasClass("pinned")&&this.hasClass("unpinned")||(this.addClass("unpinned"),this.removeClass("pinned"),this.onUnpin&&this.onUnpin.call(this))},pin:function(){this.hasClass("unpinned")&&(this.addClass("pinned"),this.removeClass("unpinned"),this.onPin&&this.onPin.call(this))},freeze:function(){this.frozen=!0,this.addClass("frozen")},unfreeze:function(){this.frozen=!1,this.removeClass("frozen")},top:function(){this.hasClass("top")||(this.addClass("top"),this.removeClass("notTop"),this.onTop&&this.onTop.call(this))},notTop:function(){this.hasClass("notTop")||(this.addClass("notTop"),this.removeClass("top"),this.onNotTop&&this.onNotTop.call(this))},bottom:function(){this.hasClass("bottom")||(this.addClass("bottom"),this.removeClass("notBottom"),this.onBottom&&this.onBottom.call(this))},notBottom:function(){this.hasClass("notBottom")||(this.addClass("notBottom"),this.removeClass("bottom"),this.onNotBottom&&this.onNotBottom.call(this))},shouldUnpin:function(t){return"down"===t.direction&&!t.top&&t.toleranceExceeded},shouldPin:function(t){return"up"===t.direction&&t.toleranceExceeded||t.top},addClass:function(t){this.elem.classList.add.apply(this.elem.classList,this.classes[t].split(" "))},removeClass:function(t){this.elem.classList.remove.apply(this.elem.classList,this.classes[t].split(" "))},hasClass:function(t){return this.classes[t].split(" ").every(function(t){return this.classList.contains(t)},this.elem)},update:function(t){t.isOutOfBounds||!0!==this.frozen&&(t.top?this.top():this.notTop(),t.bottom?this.bottom():this.notBottom(),this.shouldUnpin(t)?this.unpin():this.shouldPin(t)&&this.pin())}},o.options={tolerance:{up:0,down:0},offset:0,scroller:t()?window:null,classes:{frozen:"headroom--frozen",pinned:"headroom--pinned",unpinned:"headroom--unpinned",top:"headroom--top",notTop:"headroom--not-top",bottom:"headroom--bottom",notBottom:"headroom--not-bottom",initial:"headroom"}},o.cutsTheMustard=!!(t()&&function(){}.bind&&"classList"in document.documentElement&&Object.assign&&Object.keys&&requestAnimationFrame),o});
\ No newline at end of file
diff --git a/deps/headroom-0.11.0/jQuery.headroom.min.js b/deps/headroom-0.11.0/jQuery.headroom.min.js
new file mode 100644
index 0000000..17f70c9
--- /dev/null
+++ b/deps/headroom-0.11.0/jQuery.headroom.min.js
@@ -0,0 +1,7 @@
+/*!
+ * headroom.js v0.9.4 - Give your page some headroom. Hide your header until you need it
+ * Copyright (c) 2017 Nick Williams - http://wicky.nillia.ms/headroom.js
+ * License: MIT
+ */
+
+!function(a){a&&(a.fn.headroom=function(b){return this.each(function(){var c=a(this),d=c.data("headroom"),e="object"==typeof b&&b;e=a.extend(!0,{},Headroom.options,e),d||(d=new Headroom(this,e),d.init(),c.data("headroom",d)),"string"==typeof b&&(d[b](),"destroy"===b&&c.removeData("headroom"))})},a("[data-headroom]").each(function(){var b=a(this);b.headroom(b.data())}))}(window.Zepto||window.jQuery);
\ No newline at end of file
diff --git a/deps/search-1.0.0/autocomplete.jquery.min.js b/deps/search-1.0.0/autocomplete.jquery.min.js
new file mode 100644
index 0000000..fb63a6a
--- /dev/null
+++ b/deps/search-1.0.0/autocomplete.jquery.min.js
@@ -0,0 +1,7 @@
+/*!
+ * autocomplete.js 0.38.0
+ * https://github.com/algolia/autocomplete.js
+ * Copyright 2020 Algolia, Inc. and other contributors; Licensed MIT
+ */
+!function(a){function b(d){if(c[d])return c[d].exports;var e=c[d]={exports:{},id:d,loaded:!1};return a[d].call(e.exports,e,e.exports,b),e.loaded=!0,e.exports}var c={};b.m=a,b.c=c,b.p="",b(0)}([function(a,b,c){"use strict";a.exports=c(1)},function(a,b,c){"use strict";var d=c(2),e=c(3);d.element=e;var f=c(4);f.isArray=e.isArray,f.isFunction=e.isFunction,f.isObject=e.isPlainObject,f.bind=e.proxy,f.each=function(a,b){function c(a,c){return b(c,a)}e.each(a,c)},f.map=e.map,f.mixin=e.extend,f.Event=e.Event;var g,h,i,j=c(5),k=c(6);g=e.fn.autocomplete,h="aaAutocomplete",i={initialize:function(a,b){function c(){var c,d=e(this),f=new k({el:d});c=new j({input:d,eventBus:f,dropdownMenuContainer:a.dropdownMenuContainer,hint:void 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i[a]&&"initialize"!==a?(b=this.filter(function(){return!!e(this).data(h)}),i[a].apply(b,[].slice.call(arguments,1))):i.initialize.apply(this,arguments)},e.fn.autocomplete.noConflict=function(){return e.fn.autocomplete=g,this},e.fn.autocomplete.sources=j.sources,e.fn.autocomplete.escapeHighlightedString=f.escapeHighlightedString,a.exports=e.fn.autocomplete},function(a,b){"use strict";a.exports={element:null}},function(a,b){a.exports=jQuery},function(a,b,c){"use strict";function d(a){return a.replace(/[\-\[\]\/\{\}\(\)\*\+\?\.\\\^\$\|]/g,"\\$&")}var e=c(2);a.exports={isArray:null,isFunction:null,isObject:null,bind:null,each:null,map:null,mixin:null,isMsie:function(a){if(void 0===a&&(a=navigator.userAgent),/(msie|trident)/i.test(a)){var b=a.match(/(msie |rv:)(\d+(.\d+)?)/i);if(b)return b[2]}return!1},escapeRegExChars:function(a){return a.replace(/[\-\[\]\/\{\}\(\)\*\+\?\.\\\^\$\|]/g,"\\$&")},isNumber:function(a){return"number"==typeof a},toStr:function(a){return void 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f=document.createElement("div");f.appendChild(document.createTextNode(c));var g=document.createElement("div");return g.appendChild(document.createTextNode(a)),g.innerHTML.replace(RegExp(d(e.innerHTML),"g"),b).replace(RegExp(d(f.innerHTML),"g"),c)}}},function(a,b,c){"use strict";function d(a){var b,c;if(a=a||{},a.input||i.error("missing input"),this.isActivated=!1,this.debug=!!a.debug,this.autoselect=!!a.autoselect,this.autoselectOnBlur=!!a.autoselectOnBlur,this.openOnFocus=!!a.openOnFocus,this.minLength=i.isNumber(a.minLength)?a.minLength:1,this.autoWidth=void 0===a.autoWidth||!!a.autoWidth,this.clearOnSelected=!!a.clearOnSelected,this.tabAutocomplete=void 0===a.tabAutocomplete||!!a.tabAutocomplete,a.hint=!!a.hint,a.hint&&a.appendTo)throw new Error("[autocomplete.js] hint and appendTo options can't be used at the same 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d.Dropdown({appendTo:a.appendTo,wrapper:this.$node,menu:b,datasets:a.datasets,templates:a.templates,cssClasses:a.cssClasses,minLength:this.minLength}).onSync("suggestionClicked",this._onSuggestionClicked,this).onSync("cursorMoved",this._onCursorMoved,this).onSync("cursorRemoved",this._onCursorRemoved,this).onSync("opened",this._onOpened,this).onSync("closed",this._onClosed,this).onSync("shown",this._onShown,this).onSync("empty",this._onEmpty,this).onSync("redrawn",this._onRedrawn,this).onAsync("datasetRendered",this._onDatasetRendered,this),this.input=new 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g=n.dropdown.replace("%PREFIX%",a.cssClasses.prefix).replace("%DROPDOWN_MENU%",a.cssClasses.dropdownMenu);d=j.element(g).css(a.css.dropdown).attr({role:"listbox",id:a.listboxId}),a.templates&&a.templates.dropdownMenu&&d.html(i.templatify(a.templates.dropdownMenu)()),e=b.clone().css(a.css.hint).css(f(b)),e.val("").addClass(i.className(a.cssClasses.prefix,a.cssClasses.hint,!0)).removeAttr("id name placeholder required").prop("readonly",!0).attr({"aria-hidden":"true",autocomplete:"off",spellcheck:"false",tabindex:-1}),e.removeData&&e.removeData(),b.data(h,{"aria-autocomplete":b.attr("aria-autocomplete"),"aria-expanded":b.attr("aria-expanded"),"aria-owns":b.attr("aria-owns"),autocomplete:b.attr("autocomplete"),dir:b.attr("dir"),role:b.attr("role"),spellcheck:b.attr("spellcheck"),style:b.attr("style"),type:b.attr("type")}),b.addClass(i.className(a.cssClasses.prefix,a.cssClasses.input,!0)).attr({autocomplete:"off",spellcheck:!1,role:"combobox","aria-autocomplete":a.datasets&&a.datasets[0]&&a.datasets[0].displayKey?"both":"list","aria-expanded":"false","aria-label":a.ariaLabel,"aria-owns":a.listboxId}).css(a.hint?a.css.input:a.css.inputWithNoHint);try{b.attr("dir")||b.attr("dir","auto")}catch(a){}return c=a.appendTo?c.appendTo(j.element(a.appendTo).eq(0)).eq(0):b.wrap(c).parent(),c.prepend(a.hint?e:null).append(d),{wrapper:c,input:b,hint:e,menu:d}}function f(a){return{backgroundAttachment:a.css("background-attachment"),backgroundClip:a.css("background-clip"),backgroundColor:a.css("background-color"),backgroundImage:a.css("background-image"),backgroundOrigin:a.css("background-origin"),backgroundPosition:a.css("background-position"),backgroundRepeat:a.css("background-repeat"),backgroundSize:a.css("background-size")}}function g(a,b){var c=a.find(i.className(b.prefix,b.input));i.each(c.data(h),function(a,b){void 0===a?c.removeAttr(b):c.attr(b,a)}),c.detach().removeClass(i.className(b.prefix,b.input,!0)).insertAfter(a),c.removeData&&c.removeData(h),a.remove()}var h="aaAttrs",i=c(4),j=c(2),k=c(6),l=c(7),m=c(16),n=c(18),o=c(19);i.mixin(d.prototype,{_bindKeyboardShortcuts:function(a){if(a.keyboardShortcuts){var b=this.$input,c=[];i.each(a.keyboardShortcuts,function(a){"string"==typeof a&&(a=a.toUpperCase().charCodeAt(0)),c.push(a)}),j.element(document).keydown(function(a){var 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c=this.dropdown.getDatumForCursor(),d=this.dropdown.getCurrentCursor().attr("id");this.input.setActiveDescendant(d),c&&(b&&this.input.setInputValue(c.value,!0),this.eventBus.trigger("cursorchanged",c.raw,c.datasetName))},_onCursorRemoved:function(){this.input.resetInputValue(),this._updateHint(),this.eventBus.trigger("cursorremoved")},_onDatasetRendered:function(){this._updateHint(),this.eventBus.trigger("updated")},_onOpened:function(){this._updateHint(),this.input.expand(),this.eventBus.trigger("opened")},_onEmpty:function(){this.eventBus.trigger("empty")},_onRedrawn:function(){this.$node.css("top","0px"),this.$node.css("left","0px");var a=this.$input[0].getBoundingClientRect();this.autoWidth&&this.$node.css("width",a.width+"px");var b=this.$node[0].getBoundingClientRect(),c=a.bottom-b.top;this.$node.css("top",c+"px");var d=a.left-b.left;this.$node.css("left",d+"px"),this.eventBus.trigger("redrawn")},_onShown:function(){this.eventBus.trigger("shown"),this.autoselect&&this.dropdown.cursorTopSuggestion()},_onClosed:function(){this.input.clearHint(),this.input.removeActiveDescendant(),this.input.collapse(),this.eventBus.trigger("closed")},_onFocused:function(){if(this.isActivated=!0,this.openOnFocus){var a=this.input.getQuery();a.length>=this.minLength?this.dropdown.update(a):this.dropdown.empty(),this.dropdown.open()}},_onBlurred:function(){var a,b;a=this.dropdown.getDatumForCursor(),b=this.dropdown.getDatumForTopSuggestion();var c={selectionMethod:"blur"};this.debug||(this.autoselectOnBlur&&a?this._select(a,c):this.autoselectOnBlur&&b?this._select(b,c):(this.isActivated=!1,this.dropdown.empty(),this.dropdown.close()))},_onEnterKeyed:function(a,b){var c,d;c=this.dropdown.getDatumForCursor(),d=this.dropdown.getDatumForTopSuggestion();var 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a=this.input.getInputValue();a.length>=this.minLength?this.dropdown.update(a):this.dropdown.empty()}this.dropdown.open()},close:function(){this.dropdown.close()},setVal:function(a){a=i.toStr(a),this.isActivated?this.input.setInputValue(a):(this.input.setQuery(a),this.input.setInputValue(a,!0)),this._setLanguageDirection()},getVal:function(){return this.input.getQuery()},destroy:function(){this.input.destroy(),this.dropdown.destroy(),g(this.$node,this.cssClasses),this.$node=null},getWrapper:function(){return this.dropdown.$container[0]}}),d.Dropdown=m,d.Input=l,d.sources=c(20),a.exports=d},function(a,b,c){"use strict";function d(a){a&&a.el||e.error("EventBus initialized without el"),this.$el=f.element(a.el)}var e=c(4),f=c(2);e.mixin(d.prototype,{trigger:function(a,b,c,d){var f=e.Event("autocomplete:"+a);return this.$el.trigger(f,[b,c,d]),f}}),a.exports=d},function(a,b,c){"use strict";function d(a){var b,c,d,f,g=this;a=a||{},a.input||i.error("input is 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+var c=[].slice.call(arguments,1);this.cacheSuggestions(a,b,c),this._render.apply(this,[a,b].concat(c))}}if(this.query=a,this.canceled=!1,this.shouldFetchFromCache(a))b.apply(this,[this.cachedSuggestions].concat(this.cachedRenderExtraArgs));else{var c=this,d=function(){c.canceled||c.source(a,b.bind(c))};if(this.debounce){var e=function(){c.debounceTimeout=null,d()};clearTimeout(this.debounceTimeout),this.debounceTimeout=setTimeout(e,this.debounce)}else d()}},cacheSuggestions:function(a,b,c){this.cachedQuery=a,this.cachedSuggestions=b,this.cachedRenderExtraArgs=c},shouldFetchFromCache:function(a){return this.cache&&this.cachedQuery===a&&this.cachedSuggestions&&this.cachedSuggestions.length},clearCachedSuggestions:function(){delete this.cachedQuery,delete this.cachedSuggestions,delete this.cachedRenderExtraArgs},cancel:function(){this.canceled=!0},clear:function(){this.$el&&(this.cancel(),this.$el.empty(),this.trigger("rendered",""))},isEmpty:function(){return this._isEmpty},destroy:function(){this.clearCachedSuggestions(),this.$el=null}}),a.exports=d},function(a,b){"use strict";a.exports={wrapper:'<span class="%ROOT%"></span>',dropdown:'<span class="%PREFIX%%DROPDOWN_MENU%"></span>',dataset:'<div class="%PREFIX%%DATASET%-%CLASS%"></div>',suggestions:'<span class="%PREFIX%%SUGGESTIONS%"></span>',suggestion:'<div class="%PREFIX%%SUGGESTION%"></div>'}},function(a,b,c){"use strict";var d=c(4),e={wrapper:{position:"relative",display:"inline-block"},hint:{position:"absolute",top:"0",left:"0",borderColor:"transparent",boxShadow:"none",opacity:"1"},input:{position:"relative",verticalAlign:"top",backgroundColor:"transparent"},inputWithNoHint:{position:"relative",verticalAlign:"top"},dropdown:{position:"absolute",top:"100%",left:"0",zIndex:"100",display:"none"},suggestions:{display:"block"},suggestion:{whiteSpace:"nowrap",cursor:"pointer"},suggestionChild:{whiteSpace:"normal"},ltr:{left:"0",right:"auto"},rtl:{left:"auto",right:"0"},defaultClasses:{root:"algolia-autocomplete",prefix:"aa",noPrefix:!1,dropdownMenu:"dropdown-menu",input:"input",hint:"hint",suggestions:"suggestions",suggestion:"suggestion",cursor:"cursor",dataset:"dataset",empty:"empty"},appendTo:{wrapper:{position:"absolute",zIndex:"100",display:"none"},input:{},inputWithNoHint:{},dropdown:{display:"block"}}};d.isMsie()&&d.mixin(e.input,{backgroundImage:"url(data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)"}),d.isMsie()&&d.isMsie()<=7&&d.mixin(e.input,{marginTop:"-1px"}),a.exports=e},function(a,b,c){"use strict";a.exports={hits:c(21),popularIn:c(24)}},function(a,b,c){"use strict";var d=c(4),e=c(22),f=c(23),g=function(){function a(a,d){return window.Promise.resolve().then(function(){return b.length&&(e=a.search(b),b=[]),e}).then(function(a){if(a)return c=a.results,c[d]})}var b=[],c=[],e=window.Promise.resolve();return function(c,e){return function(f,g){a(c.as,b.push({indexName:c.indexName,query:f,params:e})-1).then(function(a){a&&g(a.hits,a)}).catch(function(a){d.error(a.message)})}}}();a.exports=function(a,b){var c=f(a.as._ua);if(c&&c[0]>=3&&c[1]>20){var d="autocomplete.js "+e;a.as._ua.indexOf(d)===-1&&(a.as._ua+="; "+d)}return g(a,b)}},function(a,b){a.exports="0.38.0"},function(a,b){"use strict";a.exports=function(a){var b=a.match(/Algolia for JavaScript \((\d+\.)(\d+\.)(\d+)\)/)||a.match(/Algolia for vanilla JavaScript (\d+\.)(\d+\.)(\d+)/);if(b)return[b[1],b[2],b[3]]}},function(a,b,c){"use strict";var d=c(4),e=c(22),f=c(23);a.exports=function(a,b,c,g){function h(h,i){a.search(h,b,function(a,h){if(a)return void d.error(a.message);if(h.hits.length>0){var l=h.hits[0],m=d.mixin({hitsPerPage:0},c);delete m.source,delete m.index;var n=f(k.as._ua);return n&&n[0]>=3&&n[1]>20&&(b.additionalUA="autocomplete.js "+e),void k.search(j(l),m,function(a,b){if(a)return void d.error(a.message);var c=[];if(g.includeAll){var e=g.allTitle||"All departments";c.push(d.mixin({facet:{value:e,count:b.nbHits}},d.cloneDeep(l)))}d.each(b.facets,function(a,b){d.each(a,function(a,e){c.push(d.mixin({facet:{facet:b,value:e,count:a}},d.cloneDeep(l)))})});for(var f=1;f<h.hits.length;++f)c.push(h.hits[f]);i(c,h)})}i([])})}var i=f(a.as._ua);if(i&&i[0]>=3&&i[1]>20&&(b=b||{},b.additionalUA="autocomplete.js "+e),!c.source)return d.error("Missing 'source' key");var j=d.isFunction(c.source)?c.source:function(a){return a[c.source]};if(!c.index)return d.error("Missing 'index' key");var k=c.index;return g=g||{},h}}]);
\ No newline at end of file
diff --git a/deps/search-1.0.0/fuse.min.js b/deps/search-1.0.0/fuse.min.js
new file mode 100644
index 0000000..7def598
--- /dev/null
+++ b/deps/search-1.0.0/fuse.min.js
@@ -0,0 +1,9 @@
+/**
+ * Fuse.js v6.4.6 - Lightweight fuzzy-search (http://fusejs.io)
+ *
+ * Copyright (c) 2021 Kiro Risk (http://kiro.me)
+ * All Rights Reserved. Apache Software License 2.0
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ */
+var e,t;e=this,t=function(){"use strict";function e(t){return(e="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(e){return typeof e}:function(e){return e&&"function"==typeof Symbol&&e.constructor===Symbol&&e!==Symbol.prototype?"symbol":typeof e})(t)}function t(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")}function n(e,t){for(var n=0;n<t.length;n++){var r=t[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(e,r.key,r)}}function r(e,t,r){return t&&n(e.prototype,t),r&&n(e,r),e}function i(e,t,n){return t in e?Object.defineProperty(e,t,{value:n,enumerable:!0,configurable:!0,writable:!0}):e[t]=n,e}function o(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter((function(t){return Object.getOwnPropertyDescriptor(e,t).enumerable}))),n.push.apply(n,r)}return n}function c(e){for(var t=1;t<arguments.length;t++){var n=null!=arguments[t]?arguments[t]:{};t%2?o(Object(n),!0).forEach((function(t){i(e,t,n[t])})):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):o(Object(n)).forEach((function(t){Object.defineProperty(e,t,Object.getOwnPropertyDescriptor(n,t))}))}return e}function a(e,t){if("function"!=typeof t&&null!==t)throw new TypeError("Super expression must either be null or a function");e.prototype=Object.create(t&&t.prototype,{constructor:{value:e,writable:!0,configurable:!0}}),t&&u(e,t)}function s(e){return(s=Object.setPrototypeOf?Object.getPrototypeOf:function(e){return e.__proto__||Object.getPrototypeOf(e)})(e)}function u(e,t){return(u=Object.setPrototypeOf||function(e,t){return e.__proto__=t,e})(e,t)}function h(e,t){return!t||"object"!=typeof t&&"function"!=typeof t?function(e){if(void 0===e)throw new ReferenceError("this hasn't been initialised - super() hasn't been called");return e}(e):t}function f(e){var t=function(){if("undefined"==typeof 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\ No newline at end of file
diff --git a/deps/search-1.0.0/mark.min.js b/deps/search-1.0.0/mark.min.js
new file mode 100644
index 0000000..1eea053
--- /dev/null
+++ b/deps/search-1.0.0/mark.min.js
@@ -0,0 +1,7 @@
+/*!***************************************************
+* mark.js v8.11.1
+* https://markjs.io/
+* Copyright (c) 2014–2018, Julian Kühnel
+* Released under the MIT license https://git.io/vwTVl
+*****************************************************/
+!function(e,t){"object"==typeof exports&&"undefined"!=typeof module?module.exports=t():"function"==typeof define&&define.amd?define(t):e.Mark=t()}(this,function(){"use strict";var e="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(e){return typeof e}:function(e){return e&&"function"==typeof Symbol&&e.constructor===Symbol&&e!==Symbol.prototype?"symbol":typeof e},t=function(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")},n=function(){function e(e,t){for(var n=0;n<t.length;n++){var r=t[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(e,r.key,r)}}return function(t,n,r){return n&&e(t.prototype,n),r&&e(t,r),t}}(),r=Object.assign||function(e){for(var t=1;t<arguments.length;t++){var n=arguments[t];for(var r in n)Object.prototype.hasOwnProperty.call(n,r)&&(e[r]=n[r])}return e},i=function(){function e(n){var r=!(arguments.length>1&&void 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index cb604a3..d3e0dec 100644
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-  <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-articles">Articles</a>
-  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
-    <a class="dropdown-item" href="articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a>
-  </div>
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles">
+<li><a class="dropdown-item" href="articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
+  </ul>
 </li>
       </ul>
-<form class="form-inline my-2 my-lg-0" role="search">
-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="search.json" id="search-input" placeholder="Search for" autocomplete="off">
-</form>
-
-      <ul class="navbar-nav">
-<li class="nav-item">
-  <a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="github">
-    <span class="fab fa fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
+<ul class="navbar-nav">
+<li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="search.json">
+</form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul>
 </div>
 
-    
+
   </div>
 </nav><div class="container template-home">
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@@ -129,21 +117,19 @@ <h2 data-toc-skip>Dev status</h2>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p>
-<p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer>
 </div>
 
-  
 
-  
+
+
 
   </body>
 </html>
diff --git a/katex-auto.js b/katex-auto.js
new file mode 100644
index 0000000..20651d9
--- /dev/null
+++ b/katex-auto.js
@@ -0,0 +1,14 @@
+// https://github.com/jgm/pandoc/blob/29fa97ab96b8e2d62d48326e1b949a71dc41f47a/src/Text/Pandoc/Writers/HTML.hs#L332-L345
+document.addEventListener("DOMContentLoaded", function () {
+  var mathElements = document.getElementsByClassName("math");
+  var macros = [];
+  for (var i = 0; i < mathElements.length; i++) {
+    var texText = mathElements[i].firstChild;
+    if (mathElements[i].tagName == "SPAN") {
+      katex.render(texText.data, mathElements[i], {
+        displayMode: mathElements[i].classList.contains("display"),
+        throwOnError: false,
+        macros: macros,
+        fleqn: false
+      });
+    }}});
diff --git a/lightswitch.js b/lightswitch.js
new file mode 100644
index 0000000..9467125
--- /dev/null
+++ b/lightswitch.js
@@ -0,0 +1,85 @@
+
+/*!
+ * Color mode toggler for Bootstrap's docs (https://getbootstrap.com/)
+ * Copyright 2011-2023 The Bootstrap Authors
+ * Licensed under the Creative Commons Attribution 3.0 Unported License.
+ * Updates for {pkgdown} by the {bslib} authors, also licensed under CC-BY-3.0.
+ */
+
+const getStoredTheme = () => localStorage.getItem('theme')
+const setStoredTheme = theme => localStorage.setItem('theme', theme)
+
+const getPreferredTheme = () => {
+  const storedTheme = getStoredTheme()
+  if (storedTheme) {
+    return storedTheme
+  }
+
+  return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light'
+}
+
+const setTheme = theme => {
+  if (theme === 'auto') {
+    document.documentElement.setAttribute('data-bs-theme', (window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light'))
+  } else {
+    document.documentElement.setAttribute('data-bs-theme', theme)
+  }
+}
+
+function bsSetupThemeToggle () {
+  'use strict'
+
+  const showActiveTheme = (theme, focus = false) => {
+    var activeLabel, activeIcon;
+
+    document.querySelectorAll('[data-bs-theme-value]').forEach(element => {
+      const buttonTheme = element.getAttribute('data-bs-theme-value')
+      const isActive = buttonTheme == theme
+      
+      element.classList.toggle('active', isActive)
+      element.setAttribute('aria-pressed', isActive)
+
+      if (isActive) {
+        activeLabel = element.textContent;
+        activeIcon = element.querySelector('span').classList.value;
+      }
+    })
+
+    const themeSwitcher = document.querySelector('#dropdown-lightswitch')
+    if (!themeSwitcher) {
+      return
+    }
+
+    themeSwitcher.setAttribute('aria-label', activeLabel)
+    themeSwitcher.querySelector('span').classList.value = activeIcon;
+
+    if (focus) {
+      themeSwitcher.focus()
+    }
+  }
+
+  window.matchMedia('(prefers-color-scheme: dark)').addEventListener('change', () => {
+    const storedTheme = getStoredTheme()
+    if (storedTheme !== 'light' && storedTheme !== 'dark') {
+      setTheme(getPreferredTheme())
+    }
+  })
+
+  window.addEventListener('DOMContentLoaded', () => {
+    showActiveTheme(getPreferredTheme())
+
+    document
+      .querySelectorAll('[data-bs-theme-value]')
+      .forEach(toggle => {
+        toggle.addEventListener('click', () => {
+          const theme = toggle.getAttribute('data-bs-theme-value')
+          setTheme(theme)
+          setStoredTheme(theme)
+          showActiveTheme(theme, true)
+        })
+      })
+  })
+}
+
+setTheme(getPreferredTheme());
+bsSetupThemeToggle();
diff --git a/pkgdown.js b/pkgdown.js
index 5fccd9c..9757bf9 100644
--- a/pkgdown.js
+++ b/pkgdown.js
@@ -30,10 +30,10 @@
   /* Clipboard --------------------------*/
 
   function changeTooltipMessage(element, msg) {
-    var tooltipOriginalTitle=element.getAttribute('data-original-title');
-    element.setAttribute('data-original-title', msg);
+    var tooltipOriginalTitle=element.getAttribute('data-bs-original-title');
+    element.setAttribute('data-bs-original-title', msg);
     $(element).tooltip('show');
-    element.setAttribute('data-original-title', tooltipOriginalTitle);
+    element.setAttribute('data-bs-original-title', tooltipOriginalTitle);
   }
 
   if(ClipboardJS.isSupported()) {
@@ -60,7 +60,7 @@
         e.clearSelection();
       });
 
-      clipboard.on('error', function() {
+      clipboard.on('error', function(e) {
         changeTooltipMessage(e.trigger,'Press Ctrl+C or Command+C to copy');
       });
 
@@ -152,5 +152,3 @@ async function searchFuse(query, callback) {
   });
   });
 })(window.jQuery || window.$)
-
-
diff --git a/pkgdown.yml b/pkgdown.yml
index 16cefb6..85eb253 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -1,10 +1,9 @@
-pandoc: 3.1.1
-pkgdown: 2.0.7.9000
-pkgdown_sha: 548a9493b72ff93d3ed8392d4ad30b77d8b15fa5
+pandoc: '3.2'
+pkgdown: 2.0.9.9000
+pkgdown_sha: a98c134518cc9f5006f9ce17d4b6fc29ea37b56b
 articles:
   data_analysis_omics_using_the_structtoolbox: data_analysis_omics_using_the_structtoolbox.html
-last_built: 2024-02-02T11:14Z
+last_built: 2024-07-01T11:36Z
 urls:
   reference: https://computational-metabolomics.github.io/structToolbox/reference
   article: https://computational-metabolomics.github.io/structToolbox/articles
-
diff --git a/reference/ANOVA.html b/reference/ANOVA.html
index 75395c7..326d7ff 100644
--- a/reference/ANOVA.html
+++ b/reference/ANOVA.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Analysis of Variance (ANOVA) is a univariate method used to analyse the difference among group means. Multiple test corrected p-values are computed to indicate significance for each feature."><title>Analysis of Variance — ANOVA • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Analysis of Variance — ANOVA"><meta property="og:description" content="Analysis of Variance (ANOVA) is a univariate method used to analyse the difference among group means. Multiple test corrected p-values are computed to indicate significance for each feature."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Analysis of Variance — ANOVA • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Analysis of Variance — ANOVA"><meta name="description" content="Analysis of Variance (ANOVA) is a univariate method used to analyse the difference among group means. Multiple test corrected p-values are computed to indicate significance for each feature."><meta property="og:description" content="Analysis of Variance (ANOVA) is a univariate method used to analyse the difference among group means. Multiple test corrected p-values are computed to indicate significance for each feature."></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary"><div class="container">
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+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.17.3</small>
+
 
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-      <ul class="navbar-nav me-auto"><li class="active nav-item">
-  <a class="nav-link" href="../reference/index.html">Reference</a>
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Analysis of Variance</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/anova_class.R" class="external-link"><code>R/anova_class.R</code></a></small>
+
+      <h1>Analysis of Variance</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/anova_class.R" class="external-link"><code>R/anova_class.R</code></a></small>
       <div class="d-none name"><code>ANOVA.Rd</code></div>
     </div>
 
@@ -59,44 +47,44 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>alpha</dt>
+
+
+<dl><dt id="arg-alpha">alpha<a class="anchor" aria-label="anchor" href="#arg-alpha"></a></dt>
 <dd><p>(numeric) The p-value cutoff for determining significance. The default is <code>0.05</code>.<br></p></dd>
 
 
-<dt>mtc</dt>
+<dt id="arg-mtc">mtc<a class="anchor" aria-label="anchor" href="#arg-mtc"></a></dt>
 <dd><p>(character) Multiple test correction method. Allowed values are limited to the following:</p><ul><li><p><code>"bonferroni"</code>: Bonferroni correction in which the p-values are multiplied by the number of comparisons.</p></li>
 <li><p><code>"fdr"</code>: Benjamini and Hochberg False Discovery Rate correction.</p></li>
 <li><p><code>"none"</code>: No correction.</p></li>
 </ul><p>The default is <code>"fdr"</code>.</p></dd>
 
 
-<dt>formula</dt>
+<dt id="arg-formula">formula<a class="anchor" aria-label="anchor" href="#arg-formula"></a></dt>
 <dd><p>(formula) A symbolic description of the model to be fitted.</p></dd>
 
 
-<dt>ss_type</dt>
+<dt id="arg-ss-type">ss_type<a class="anchor" aria-label="anchor" href="#arg-ss-type"></a></dt>
 <dd><p>(character) ANOVA sum of squares. Allowed values are limited to the following:</p><ul><li><p><code>"I"</code>: Type I sum of squares.</p></li>
 <li><p><code>"II"</code>: Type II sum of squares.</p></li>
 <li><p><code>"III"</code>: Type III sum of squares.</p></li>
 </ul><p>The default is <code>"III"</code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>ANOVA</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>f_statistic</code></td><td>(data.frame) The value of the calculated statistic.</td></tr><tr><td><code>p_value</code></td><td>(data.frame) The probability of observing the calculated statistic if the null hypothesis is true.</td></tr><tr><td><code>significant</code></td><td>(data.frame) True/False indicating whether the p-value computed for each variable is less than the threshold.</td></tr></table></div>
+    <p>A  <code>ANOVA</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>f_statistic</code></td><td>(data.frame) The value of the calculated statistic.</td></tr><tr><td><code>p_value</code></td><td>(data.frame) The probability of observing the calculated statistic if the null hypothesis is true.</td></tr><tr><td><code>significant</code></td><td>(data.frame) True/False indicating whether the p-value computed for each variable is less than the threshold.</td></tr></table></div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>car</code></p></li>
 </ul></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>ANOVA</code> object inherits the following <code>struct</code> classes: <br><br><code>[ANOVA]</code> &gt;&gt; <code>[model]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -120,23 +108,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="model_apply.html">model_apply</a></span><span class="op">(</span><span class="va">M</span>,<span class="va">D</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
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-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
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diff --git a/reference/AUC.html b/reference/AUC.html
index 06c57a6..a37b8a6 100644
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+++ b/reference/AUC.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="The area under the ROC curve of a classifier is estimated using the trapezoid method."><title>Area under ROC curve — AUC • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Area under ROC curve — AUC"><meta property="og:description" content="The area under the ROC curve of a classifier is estimated using the trapezoid method."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Area under ROC curve — AUC • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Area under ROC curve — AUC"><meta name="description" content="The area under the ROC curve of a classifier is estimated using the trapezoid method."><meta property="og:description" content="The area under the ROC curve of a classifier is estimated using the trapezoid method."></head><body>
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     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
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+
 
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+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Area under ROC curve</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/AUC_metric_class.R" class="external-link"><code>R/AUC_metric_class.R</code></a></small>
+
+      <h1>Area under ROC curve</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/AUC_metric_class.R" class="external-link"><code>R/AUC_metric_class.R</code></a></small>
       <div class="d-none name"><code>AUC.Rd</code></div>
     </div>
 
@@ -59,21 +47,21 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>...</dt>
+
+
+<dl><dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>
+    <p>A  <code>
 AUC
 </code> object. This object has no <code>output</code> slots.</p>
     </div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>AUC</code> object inherits the following <code>struct</code> classes: <br><br><code>[AUC]</code> &gt;&gt; <code>[metric]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -113,23 +101,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>‘&gt;=’ not meaningful for factors</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
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-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
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-  
 
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diff --git a/reference/DFA.html b/reference/DFA.html
index 8efb667..88b4ab9 100644
--- a/reference/DFA.html
+++ b/reference/DFA.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Discriminant Factor Analysis (DFA) is a supervised classification method. Using a linear combination of the input variables, DFA finds new orthogonal axes (canonical values) to minimize the variance within each given class and maximize variance between classes."><title>Discriminant Factor Analysis — DFA • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Discriminant Factor Analysis — DFA"><meta property="og:description" content="Discriminant Factor Analysis (DFA) is a supervised classification method. Using a linear combination of the input variables, DFA finds new orthogonal axes (canonical values) to minimize the variance within each given class and maximize variance between classes."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Discriminant Factor Analysis — DFA • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Discriminant Factor Analysis — DFA"><meta name="description" content="Discriminant Factor Analysis (DFA) is a supervised classification method. Using a linear combination of the input variables, DFA finds new orthogonal axes (canonical values) to minimize the variance within each given class and maximize variance between classes."><meta property="og:description" content="Discriminant Factor Analysis (DFA) is a supervised classification method. Using a linear combination of the input variables, DFA finds new orthogonal axes (canonical values) to minimize the variance within each given class and maximize variance between classes."></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
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+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.17.3</small>
+
 
-    
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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     <div id="navbar" class="collapse navbar-collapse ms-3">
-      <ul class="navbar-nav me-auto"><li class="active nav-item">
-  <a class="nav-link" href="../reference/index.html">Reference</a>
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
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-    <a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a>
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-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
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-     
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-</li>
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Discriminant Factor Analysis</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/DFA_class.R" class="external-link"><code>R/DFA_class.R</code></a></small>
+
+      <h1>Discriminant Factor Analysis</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/DFA_class.R" class="external-link"><code>R/DFA_class.R</code></a></small>
       <div class="d-none name"><code>DFA.Rd</code></div>
     </div>
 
@@ -59,26 +47,26 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>factor_name</dt>
+
+
+<dl><dt id="arg-factor-name">factor_name<a class="anchor" aria-label="anchor" href="#arg-factor-name"></a></dt>
 <dd><p>(character) The name of a sample-meta column to use.</p></dd>
 
 
-<dt>number_components</dt>
+<dt id="arg-number-components">number_components<a class="anchor" aria-label="anchor" href="#arg-number-components"></a></dt>
 <dd><p>(numeric, integer) The number of DFA components calculated. The default is <code>2</code>.<br></p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>DFA</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>scores</code></td><td>(DatasetExperiment)</td></tr><tr><td><code>loadings</code></td><td>(data.frame)</td></tr><tr><td><code>eigenvalues</code></td><td>(data.frame)</td></tr><tr><td><code>that</code></td><td>(DatasetExperiment)</td></tr></table></div>
+    <p>A  <code>DFA</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>scores</code></td><td>(DatasetExperiment)</td></tr><tr><td><code>loadings</code></td><td>(data.frame)</td></tr><tr><td><code>eigenvalues</code></td><td>(data.frame)</td></tr><tr><td><code>that</code></td><td>(DatasetExperiment)</td></tr></table></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>DFA</code> object inherits the following <code>struct</code> classes: <br><br><code>[DFA]</code> &gt;&gt; <code>[model]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -99,23 +87,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="model_apply.html">model_apply</a></span><span class="op">(</span><span class="va">M</span>,<span class="va">D</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
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diff --git a/reference/DatasetExperiment_boxplot-1.png b/reference/DatasetExperiment_boxplot-1.png
index 3a96ec6..f5f640c 100644
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diff --git a/reference/DatasetExperiment_boxplot.html b/reference/DatasetExperiment_boxplot.html
index 0d38061..674aaba 100644
--- a/reference/DatasetExperiment_boxplot.html
+++ b/reference/DatasetExperiment_boxplot.html
@@ -1,50 +1,38 @@
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Feature distribution histogram</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/dataset_chart_classes.R" class="external-link"><code>R/dataset_chart_classes.R</code></a></small>
+
+      <h1>Feature distribution histogram</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/dataset_chart_classes.R" class="external-link"><code>R/dataset_chart_classes.R</code></a></small>
       <div class="d-none name"><code>DatasetExperiment_boxplot.Rd</code></div>
     </div>
 
@@ -65,45 +53,43 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>factor_name</dt>
+
+
+<dl><dt id="arg-factor-name">factor_name<a class="anchor" aria-label="anchor" href="#arg-factor-name"></a></dt>
 <dd><p>(character) The name of a sample-meta column to use.</p></dd>
 
 
-<dt>by_sample</dt>
+<dt id="arg-by-sample">by_sample<a class="anchor" aria-label="anchor" href="#arg-by-sample"></a></dt>
 <dd><p>(logical) Plot by sample. Allowed values are limited to the following:</p><ul><li><p><code>"TRUE"</code>: The data is plotted across features for a subset of samples.</p></li>
 <li><p><code>"FALSE"</code>: The data is plotted across samples for a subset of features.</p></li>
 </ul><p>The default is <code>TRUE</code>.<br></p></dd>
 
 
-<dt>per_class</dt>
+<dt id="arg-per-class">per_class<a class="anchor" aria-label="anchor" href="#arg-per-class"></a></dt>
 <dd><p>(logical) Plot per class. Allowed values are limited to the following:</p><ul><li><p><code>"TRUE"</code>: The data is plotted for each class.</p></li>
 <li><p><code>"FALSE"</code>: The data is plotted for all samples.</p></li>
 </ul><p>The default is <code>TRUE</code>.<br></p></dd>
 
 
-<dt>number</dt>
+<dt id="arg-number">number<a class="anchor" aria-label="anchor" href="#arg-number"></a></dt>
 <dd><p>(numeric, integer) The number of features/samples plotted. The default is <code>50</code>.<br></p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>
+    <p>A  <code>
 DatasetExperiment_boxplot
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
-
-
 <p>struct object</p>
     </div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>DatasetExperiment_boxplot</code> object inherits the following <code>struct</code> classes: <br><br><code>[DatasetExperiment_boxplot]</code> &gt;&gt; <code>[chart]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -119,27 +105,28 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu"><a href="MTBLS79_DatasetExperiment.html">MTBLS79_DatasetExperiment</a></span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">DatasetExperiment_boxplot</span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'Class'</span>,number<span class="op">=</span><span class="fl">10</span>,per_class<span class="op">=</span><span class="cn">FALSE</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="chart_plot.html">chart_plot</a></span><span class="op">(</span><span class="va">C</span>,<span class="va">D</span><span class="op">)</span></span></span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Removed 585 rows containing non-finite values (`stat_boxplot()`).</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Removed 585 rows containing non-finite outside the scale range</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> (`stat_boxplot()`).</span>
 <span class="r-plt img"><img src="DatasetExperiment_boxplot-1.png" alt="" width="700" height="433"></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
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diff --git a/reference/DatasetExperiment_dist-1.png b/reference/DatasetExperiment_dist-1.png
index c6c042b..fe01243 100644
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diff --git a/reference/DatasetExperiment_dist.html b/reference/DatasetExperiment_dist.html
index 7385530..65418be 100644
--- a/reference/DatasetExperiment_dist.html
+++ b/reference/DatasetExperiment_dist.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="A histogram to visualise the distribution of values within features."><title>Feature distribution histogram — DatasetExperiment_dist • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Feature distribution histogram — DatasetExperiment_dist"><meta property="og:description" content="A histogram to visualise the distribution of values within features."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Feature distribution histogram — DatasetExperiment_dist • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Feature distribution histogram — DatasetExperiment_dist"><meta name="description" content="A histogram to visualise the distribution of values within features."><meta property="og:description" content="A histogram to visualise the distribution of values within features."></head><body>
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     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.17.3</small>
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Feature distribution histogram</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/dataset_chart_classes.R" class="external-link"><code>R/dataset_chart_classes.R</code></a></small>
+
+      <h1>Feature distribution histogram</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/dataset_chart_classes.R" class="external-link"><code>R/dataset_chart_classes.R</code></a></small>
       <div class="d-none name"><code>DatasetExperiment_dist.Rd</code></div>
     </div>
 
@@ -59,32 +47,32 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>factor_name</dt>
+
+
+<dl><dt id="arg-factor-name">factor_name<a class="anchor" aria-label="anchor" href="#arg-factor-name"></a></dt>
 <dd><p>(character) The name of a sample-meta column to use.</p></dd>
 
 
-<dt>per_class</dt>
+<dt id="arg-per-class">per_class<a class="anchor" aria-label="anchor" href="#arg-per-class"></a></dt>
 <dd><p>(logical) Plot per class. Allowed values are limited to the following:</p><ul><li><p><code>"TRUE"</code>: The distributions are plotted for each class.</p></li>
 <li><p><code>"FALSE"</code>: The distribution is plotted for all samples.</p></li>
 </ul><p>The default is <code>TRUE</code>.<br></p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>
+    <p>A  <code>
 DatasetExperiment_dist
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>DatasetExperiment_dist</code> object inherits the following <code>struct</code> classes: <br><br><code>[DatasetExperiment_dist]</code> &gt;&gt; <code>[chart]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -98,27 +86,27 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu"><a href="MTBLS79_DatasetExperiment.html">MTBLS79_DatasetExperiment</a></span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">DatasetExperiment_dist</span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'Class'</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="chart_plot.html">chart_plot</a></span><span class="op">(</span><span class="va">C</span>,<span class="va">D</span><span class="op">)</span></span></span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Removed 9222 rows containing non-finite values (`stat_bin()`).</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Removed 9222 rows containing non-finite outside the scale range (`stat_bin()`).</span>
 <span class="r-plt img"><img src="DatasetExperiment_dist-1.png" alt="" width="700" height="433"></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
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diff --git a/reference/DatasetExperiment_factor_boxplot-1.png b/reference/DatasetExperiment_factor_boxplot-1.png
index ccea6eb..5b1e127 100644
Binary files a/reference/DatasetExperiment_factor_boxplot-1.png and b/reference/DatasetExperiment_factor_boxplot-1.png differ
diff --git a/reference/DatasetExperiment_factor_boxplot.html b/reference/DatasetExperiment_factor_boxplot.html
index bc4f34e..a4d620a 100644
--- a/reference/DatasetExperiment_factor_boxplot.html
+++ b/reference/DatasetExperiment_factor_boxplot.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Boxplot for a feature to visualise the distribution of values within each group"><title>Factor boxplot — DatasetExperiment_factor_boxplot • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Factor boxplot — DatasetExperiment_factor_boxplot"><meta property="og:description" content="Boxplot for a feature to visualise the distribution of values within each group"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Factor boxplot</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/factor_barchart_class.R" class="external-link"><code>R/factor_barchart_class.R</code></a></small>
+
+      <h1>Factor boxplot</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/factor_barchart_class.R" class="external-link"><code>R/factor_barchart_class.R</code></a></small>
       <div class="d-none name"><code>DatasetExperiment_factor_boxplot.Rd</code></div>
     </div>
 
@@ -59,30 +47,30 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>feature_to_plot</dt>
+
+
+<dl><dt id="arg-feature-to-plot">feature_to_plot<a class="anchor" aria-label="anchor" href="#arg-feature-to-plot"></a></dt>
 <dd><p>(character, numeric, integer) The name of the plotted feature.</p></dd>
 
 
-<dt>factor_names</dt>
+<dt id="arg-factor-names">factor_names<a class="anchor" aria-label="anchor" href="#arg-factor-names"></a></dt>
 <dd><p>(character) The name of sample meta column(s) to use.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>
+    <p>A  <code>
 DatasetExperiment_factor_boxplot
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>DatasetExperiment_factor_boxplot</code> object inherits the following <code>struct</code> classes: <br><br><code>[DatasetExperiment_factor_boxplot]</code> &gt;&gt; <code>[chart]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -99,23 +87,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-plt img"><img src="DatasetExperiment_factor_boxplot-1.png" alt="" width="700" height="433"></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/DatasetExperiment_heatmap-1.png b/reference/DatasetExperiment_heatmap-1.png
index f879f9a..dc7ffed 100644
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diff --git a/reference/DatasetExperiment_heatmap.html b/reference/DatasetExperiment_heatmap.html
index 42d5c74..0a0e82e 100644
--- a/reference/DatasetExperiment_heatmap.html
+++ b/reference/DatasetExperiment_heatmap.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
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     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
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+
 
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 <li class="nav-item dropdown">
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
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+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>DatasetExperiment heatmap</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/dataset_chart_classes.R" class="external-link"><code>R/dataset_chart_classes.R</code></a></small>
+
+      <h1>DatasetExperiment heatmap</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/dataset_chart_classes.R" class="external-link"><code>R/dataset_chart_classes.R</code></a></small>
       <div class="d-none name"><code>DatasetExperiment_heatmap.Rd</code></div>
     </div>
 
@@ -59,19 +47,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>na_colour</dt>
+
+
+<dl><dt id="arg-na-colour">na_colour<a class="anchor" aria-label="anchor" href="#arg-na-colour"></a></dt>
 <dd><p>(character) The hex colour code used to plot missing values. The default is <code>"#FF00E4"</code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>
+    <p>A  <code>
 DatasetExperiment_heatmap
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
@@ -82,7 +70,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 </ul></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>DatasetExperiment_heatmap</code> object inherits the following <code>struct</code> classes: <br><br><code>[DatasetExperiment_heatmap]</code> &gt;&gt; <code>[chart]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -104,23 +92,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-plt img"><img src="DatasetExperiment_heatmap-1.png" alt="" width="700" height="433"></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
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-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
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diff --git a/reference/HCA.html b/reference/HCA.html
index aa8e36d..d78001d 100644
--- a/reference/HCA.html
+++ b/reference/HCA.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Hierarchical Cluster Analysis is a numerical technique that uses agglomerative clustering to identify clusters or groupings of samples."><title>Hierarchical Cluster Analysis — HCA • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Hierarchical Cluster Analysis — HCA"><meta property="og:description" content="Hierarchical Cluster Analysis is a numerical technique that uses agglomerative clustering to identify clusters or groupings of samples."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
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-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Hierarchical Cluster Analysis — HCA • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Hierarchical Cluster Analysis — HCA"><meta name="description" content="Hierarchical Cluster Analysis is a numerical technique that uses agglomerative clustering to identify clusters or groupings of samples."><meta property="og:description" content="Hierarchical Cluster Analysis is a numerical technique that uses agglomerative clustering to identify clusters or groupings of samples."></head><body>
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     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.17.3</small>
+
 
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+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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 </nav><div class="container template-reference-topic">
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Hierarchical Cluster Analysis</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/hca_class.R" class="external-link"><code>R/hca_class.R</code></a></small>
+
+      <h1>Hierarchical Cluster Analysis</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/hca_class.R" class="external-link"><code>R/hca_class.R</code></a></small>
       <div class="d-none name"><code>HCA.Rd</code></div>
     </div>
 
@@ -65,7 +53,9 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>dist_method</dt>
+
+
+<dl><dt id="arg-dist-method">dist_method<a class="anchor" aria-label="anchor" href="#arg-dist-method"></a></dt>
 <dd><p>(character) Distance measure. Allowed values are limited to the following:</p><ul><li><p><code>"euclidean"</code>: The euclidean distance (2 norm).</p></li>
 <li><p><code>"maximum"</code>: The maximum distance.</p></li>
 <li><p><code>"manhattan"</code>: The absolute distance (1 norm).</p></li>
@@ -74,7 +64,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </ul><p>The default is <code>"euclidean"</code>.</p></dd>
 
 
-<dt>cluster_method</dt>
+<dt id="arg-cluster-method">cluster_method<a class="anchor" aria-label="anchor" href="#arg-cluster-method"></a></dt>
 <dd><p>(character) Agglomeration method. Allowed values are limited to the following:</p><ul><li><p><code>"ward.D"</code>: Ward clustering.</p></li>
 <li><p><code>"ward.D2"</code>: Ward clustering using sqaured distances.</p></li>
 <li><p><code>"single"</code>: Single linkage.</p></li>
@@ -86,36 +76,34 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </ul><p>The default is <code>"complete"</code>.</p></dd>
 
 
-<dt>minkowski_power</dt>
+<dt id="arg-minkowski-power">minkowski_power<a class="anchor" aria-label="anchor" href="#arg-minkowski-power"></a></dt>
 <dd><p>(numeric)  The default is <code>2</code>.<br></p></dd>
 
 
-<dt>factor_name</dt>
+<dt id="arg-factor-name">factor_name<a class="anchor" aria-label="anchor" href="#arg-factor-name"></a></dt>
 <dd><p>(character) The name of a sample-meta column to use.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>HCA</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>dist_matrix</code></td><td>(dist) An object containing pairwise distance information between samples.</td></tr><tr><td><code>hclust</code></td><td>(hclust) An object of class hclust which describes the tree produced by the clustering process.</td></tr><tr><td><code>factor_df</code></td><td>(data.frame)</td></tr></table></div>
+    <p>A  <code>HCA</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>dist_matrix</code></td><td>(dist) An object containing pairwise distance information between samples.</td></tr><tr><td><code>hclust</code></td><td>(hclust) An object of class hclust which describes the tree produced by the clustering process.</td></tr><tr><td><code>factor_df</code></td><td>(data.frame)</td></tr></table></div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>stats</code></p></li>
 </ul></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>HCA</code> object inherits the following <code>struct</code> classes: <br><br><code>[HCA]</code> &gt;&gt; <code>[model]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
     <div class="section level2">
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
-    <p>R Core Team (2023). <em>R: A Language and Environment for Statistical
+    <p>R Core Team (2024). <em>R: A Language and Environment for Statistical
 Computing</em>. R Foundation for Statistical Computing, Vienna, Austria.
 <a href="https://www.R-project.org/" class="external-link">https://www.R-project.org/</a>.</p>
     </div>
@@ -134,23 +122,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
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diff --git a/reference/HSD.html b/reference/HSD.html
index 51a3f5e..1f43d5d 100644
--- a/reference/HSD.html
+++ b/reference/HSD.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Tukey's HSD post hoc test is a modified t-test applied for all features to all pairs of levels in a factor. It is used to determine which groups are different (if any). A multiple test corrected p-value is computed to indicate which groups are significantly different to the others for each feature."><title>Tukey's Honest Significant Difference — HSD • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Tukey's Honest Significant Difference — HSD"><meta property="og:description" content="Tukey's HSD post hoc test is a modified t-test applied for all features to all pairs of levels in a factor. It is used to determine which groups are different (if any). A multiple test corrected p-value is computed to indicate which groups are significantly different to the others for each feature."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Tukey's Honest Significant Difference</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/HSD_class.R" class="external-link"><code>R/HSD_class.R</code></a></small>
+
+      <h1>Tukey's Honest Significant Difference</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/HSD_class.R" class="external-link"><code>R/HSD_class.R</code></a></small>
       <div class="d-none name"><code>HSD.Rd</code></div>
     </div>
 
@@ -59,43 +47,43 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>alpha</dt>
+
+
+<dl><dt id="arg-alpha">alpha<a class="anchor" aria-label="anchor" href="#arg-alpha"></a></dt>
 <dd><p>(numeric) The p-value cutoff for determining significance. The default is <code>0.05</code>.<br></p></dd>
 
 
-<dt>mtc</dt>
+<dt id="arg-mtc">mtc<a class="anchor" aria-label="anchor" href="#arg-mtc"></a></dt>
 <dd><p>(character) Multiple test correction method. Allowed values are limited to the following:</p><ul><li><p><code>"bonferroni"</code>: Bonferroni correction in which the p-values are multiplied by the number of comparisons.</p></li>
 <li><p><code>"fdr"</code>: Benjamini and Hochberg False Discovery Rate correction.</p></li>
 <li><p><code>"none"</code>: No correction.</p></li>
 </ul><p>The default is <code>"fdr"</code>.</p></dd>
 
 
-<dt>formula</dt>
+<dt id="arg-formula">formula<a class="anchor" aria-label="anchor" href="#arg-formula"></a></dt>
 <dd><p>(formula) A symbolic description of the model to be fitted.</p></dd>
 
 
-<dt>unbalanced</dt>
+<dt id="arg-unbalanced">unbalanced<a class="anchor" aria-label="anchor" href="#arg-unbalanced"></a></dt>
 <dd><p>(logical) Unbalanced model. Allowed values are limited to the following:</p><ul><li><p><code>"TRUE"</code>: A correction is applied for unbalanced designs.</p></li>
 <li><p><code>"FALSE"</code>: No correction is applied for unbalanced designs.</p></li>
 </ul><p>The default is <code>FALSE</code>.<br></p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>HSD</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>difference</code></td><td>(data.frame)</td></tr><tr><td><code>UCL</code></td><td>(data.frame)</td></tr><tr><td><code>LCL</code></td><td>(data.frame)</td></tr><tr><td><code>p_value</code></td><td>(data.frame) The probability of observing the calculated statistic if the null hypothesis is true.</td></tr><tr><td><code>significant</code></td><td>(data.frame) True/False indicating whether the p-value computed for each variable is less than the threshold.</td></tr></table></div>
+    <p>A  <code>HSD</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>difference</code></td><td>(data.frame)</td></tr><tr><td><code>UCL</code></td><td>(data.frame)</td></tr><tr><td><code>LCL</code></td><td>(data.frame)</td></tr><tr><td><code>p_value</code></td><td>(data.frame) The probability of observing the calculated statistic if the null hypothesis is true.</td></tr><tr><td><code>significant</code></td><td>(data.frame) True/False indicating whether the p-value computed for each variable is less than the threshold.</td></tr></table></div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>agricolae</code></p></li>
 </ul></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>HSD</code> object inherits the following <code>struct</code> classes: <br><br><code>[HSD]</code> &gt;&gt; <code>[model]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -119,23 +107,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="model_apply.html">model_apply</a></span><span class="op">(</span><span class="va">M</span>,<span class="va">D</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
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diff --git a/reference/HSDEM.html b/reference/HSDEM.html
index 5988d93..80ba2f9 100644
--- a/reference/HSDEM.html
+++ b/reference/HSDEM.html
@@ -1,50 +1,38 @@
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-      <img src="" class="logo" alt=""><h1>Tukey's Honest Significant Difference using estimated marginal means</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/HSDEM_class.R" class="external-link"><code>R/HSDEM_class.R</code></a></small>
+
+      <h1>Tukey's Honest Significant Difference using estimated marginal means</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/HSDEM_class.R" class="external-link"><code>R/HSDEM_class.R</code></a></small>
       <div class="d-none name"><code>HSDEM.Rd</code></div>
     </div>
 
@@ -59,30 +47,30 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>alpha</dt>
+
+
+<dl><dt id="arg-alpha">alpha<a class="anchor" aria-label="anchor" href="#arg-alpha"></a></dt>
 <dd><p>(numeric) The p-value cutoff for determining significance. The default is <code>0.05</code>.<br></p></dd>
 
 
-<dt>mtc</dt>
+<dt id="arg-mtc">mtc<a class="anchor" aria-label="anchor" href="#arg-mtc"></a></dt>
 <dd><p>(character) Multiple test correction method. Allowed values are limited to the following:</p><ul><li><p><code>"bonferroni"</code>: Bonferroni correction in which the p-values are multiplied by the number of comparisons.</p></li>
 <li><p><code>"fdr"</code>: Benjamini and Hochberg False Discovery Rate correction.</p></li>
 <li><p><code>"none"</code>: No correction.</p></li>
 </ul><p>The default is <code>"fdr"</code>.</p></dd>
 
 
-<dt>formula</dt>
+<dt id="arg-formula">formula<a class="anchor" aria-label="anchor" href="#arg-formula"></a></dt>
 <dd><p>(formula) A symbolic description of the model to be fitted.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>HSDEM</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>p_value</code></td><td>(data.frame) The probability of observing the calculated statistic if the null hypothesis is true.</td></tr><tr><td><code>significant</code></td><td>(data.frame) True/False indicating whether the p-value computed for each variable is less than the threshold.</td></tr></table></div>
+    <p>A  <code>HSDEM</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>p_value</code></td><td>(data.frame) The probability of observing the calculated statistic if the null hypothesis is true.</td></tr><tr><td><code>significant</code></td><td>(data.frame) True/False indicating whether the p-value computed for each variable is less than the threshold.</td></tr></table></div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>emmeans</code></p></li>
@@ -90,14 +78,14 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 </ul></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>HSDEM</code> object inherits the following <code>struct</code> classes: <br><br><code>[HSDEM]</code> &gt;&gt; <code>[model]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
     <div class="section level2">
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
-    <p>Lenth R (2023). <em>emmeans: Estimated Marginal Means, aka Least-Squares
-Means</em>. R package version 1.9.0,
+    <p>Lenth R (2024). <em>emmeans: Estimated Marginal Means, aka Least-Squares
+Means</em>. R package version 1.10.1,
 <a href="https://CRAN.R-project.org/package=emmeans" class="external-link">https://CRAN.R-project.org/package=emmeans</a>.</p>
 <p>Pinheiro J, Bates D, R Core Team (2023). <em>nlme: Linear and Nonlinear
 Mixed Effects Models</em>. R package version 3.1-164,
@@ -121,23 +109,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
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+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
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+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
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diff --git a/reference/MTBLS79_DatasetExperiment.html b/reference/MTBLS79_DatasetExperiment.html
index b4aa1a6..db2c3fd 100644
--- a/reference/MTBLS79_DatasetExperiment.html
+++ b/reference/MTBLS79_DatasetExperiment.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Direct-infusion mass spectrometry (DIMS) metabolomics is an important approach
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>MTBLS79: Direct infusion mass spectrometry metabolomics dataset: a benchmark for data processing and quality control — MTBLS79_DatasetExperiment • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="MTBLS79: Direct infusion mass spectrometry metabolomics dataset: a benchmark for data processing and quality control — MTBLS79_DatasetExperiment"><meta name="description" content="Direct-infusion mass spectrometry (DIMS) metabolomics is an important approach
 for characterising molecular responses of organisms to disease, drugs and the
 environment. Increasingly large-scale metabolomics studies are being conducted,
 necessitating improvements in both bioanalytical and computational workflows
@@ -8,7 +8,7 @@
 extracts. It comprises of twenty biological samples (cow vs. sheep) that were
 analysed repeatedly, in 8 batches across 7 days, together with a concurrent set
 of quality control (QC) samples. Data are presented from each step of the workflow
-and are available in MetaboLights (https://www.ebi.ac.uk/metabolights/MTBLS79)"><title>MTBLS79: Direct infusion mass spectrometry metabolomics dataset: a benchmark for data processing and quality control — MTBLS79_DatasetExperiment • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="MTBLS79: Direct infusion mass spectrometry metabolomics dataset: a benchmark for data processing and quality control — MTBLS79_DatasetExperiment"><meta property="og:description" content="Direct-infusion mass spectrometry (DIMS) metabolomics is an important approach
+and are available in MetaboLights (https://www.ebi.ac.uk/metabolights/MTBLS79)"><meta property="og:description" content="Direct-infusion mass spectrometry (DIMS) metabolomics is an important approach
 for characterising molecular responses of organisms to disease, drugs and the
 environment. Increasingly large-scale metabolomics studies are being conducted,
 necessitating improvements in both bioanalytical and computational workflows
@@ -17,52 +17,40 @@
 extracts. It comprises of twenty biological samples (cow vs. sheep) that were
 analysed repeatedly, in 8 batches across 7 days, together with a concurrent set
 of quality control (QC) samples. Data are presented from each step of the workflow
-and are available in MetaboLights (https://www.ebi.ac.uk/metabolights/MTBLS79)"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--></head><body>
+and are available in MetaboLights (https://www.ebi.ac.uk/metabolights/MTBLS79)"></head><body>
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
-  <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-articles">Articles</a>
-  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
-    <a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a>
-  </div>
-</li>
-      </ul><form class="form-inline my-2 my-lg-0" role="search">
-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
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-    <span class="fab fa fab fa-github fa-lg"></span>
-     
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-</li>
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>MTBLS79: Direct infusion mass spectrometry metabolomics dataset: a benchmark for data processing and quality control</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/MTBLS79_dataset_class.R" class="external-link"><code>R/MTBLS79_dataset_class.R</code></a></small>
+
+      <h1>MTBLS79: Direct infusion mass spectrometry metabolomics dataset: a benchmark for data processing and quality control</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/MTBLS79_dataset_class.R" class="external-link"><code>R/MTBLS79_dataset_class.R</code></a></small>
       <div class="d-none name"><code>MTBLS79_DatasetExperiment.Rd</code></div>
     </div>
 
@@ -86,7 +74,9 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>filtered</dt>
+
+
+<dl><dt id="arg-filtered">filtered<a class="anchor" aria-label="anchor" href="#arg-filtered"></a></dt>
 <dd><p>TRUE to load data with quality control filters already applied,
 or FALSE to load the unfiltered data. Default is FALSE. The raw data is available
 from (https://www.ebi.ac.uk/metabolights/MTBLS79) and as an R dataset in the
@@ -95,9 +85,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>DatasetExperiment object</p>
+    <p>DatasetExperiment object</p>
     </div>
 
     <div class="section level2">
@@ -107,23 +95,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] "DatasetExperiment object of length 172 with 7 metadata columns"</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
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diff --git a/reference/OPLSDA.html b/reference/OPLSDA.html
index a340156..034db16 100644
--- a/reference/OPLSDA.html
+++ b/reference/OPLSDA.html
@@ -1,52 +1,40 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="OPLS splits a data matrix into two parts. One part
-contains information orthogonal to the input vector, and the other is non-orthogonal."><title>Orthogonal Partial Least Squares regression — OPLSDA • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Orthogonal Partial Least Squares regression — OPLSDA"><meta property="og:description" content="OPLS splits a data matrix into two parts. One part
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Orthogonal Partial Least Squares regression — OPLSDA • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Orthogonal Partial Least Squares regression — OPLSDA"><meta name="description" content="OPLS splits a data matrix into two parts. One part
+contains information orthogonal to the input vector, and the other is non-orthogonal."><meta property="og:description" content="OPLS splits a data matrix into two parts. One part
+contains information orthogonal to the input vector, and the other is non-orthogonal."></head><body>
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+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Orthogonal Partial Least Squares regression</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/oplsda_class.R" class="external-link"><code>R/oplsda_class.R</code></a></small>
+
+      <h1>Orthogonal Partial Least Squares regression</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/oplsda_class.R" class="external-link"><code>R/oplsda_class.R</code></a></small>
       <div class="d-none name"><code>OPLSDA.Rd</code></div>
     </div>
 
@@ -62,26 +50,26 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>number_components</dt>
+
+
+<dl><dt id="arg-number-components">number_components<a class="anchor" aria-label="anchor" href="#arg-number-components"></a></dt>
 <dd><p>(numeric, integer) The number of orthgonal components. The default is <code>1</code>.<br></p></dd>
 
 
-<dt>factor_name</dt>
+<dt id="arg-factor-name">factor_name<a class="anchor" aria-label="anchor" href="#arg-factor-name"></a></dt>
 <dd><p>(character) The name of a sample-meta column to use.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>OPLSDA</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>opls_model</code></td><td>(list)</td></tr><tr><td><code>filtered</code></td><td>(DatasetExperiment)</td></tr><tr><td><code>orthogonal</code></td><td>(DatasetExperiment)</td></tr></table></div>
+    <p>A  <code>OPLSDA</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>opls_model</code></td><td>(list)</td></tr><tr><td><code>filtered</code></td><td>(DatasetExperiment)</td></tr><tr><td><code>orthogonal</code></td><td>(DatasetExperiment)</td></tr></table></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>OPLSDA</code> object inherits the following <code>struct</code> classes: <br><br><code>[OPLSDA]</code> &gt;&gt; <code>[OPLSR]</code> &gt;&gt; <code>[model]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -95,23 +83,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="OPLSR.html">OPLSR</a></span><span class="op">(</span><span class="st">'number_components'</span><span class="op">=</span><span class="fl">2</span>,factor_name<span class="op">=</span><span class="st">'Species'</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
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+
 
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diff --git a/reference/OPLSR.html b/reference/OPLSR.html
index d64eec4..aaf7838 100644
--- a/reference/OPLSR.html
+++ b/reference/OPLSR.html
@@ -1,52 +1,40 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="OPLS splits a data matrix into two parts. One part
-contains information orthogonal to the input vector, and the other is non-orthogonal."><title>Orthogonal Partial Least Squares regression — OPLSR • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Orthogonal Partial Least Squares regression — OPLSR"><meta property="og:description" content="OPLS splits a data matrix into two parts. One part
-contains information orthogonal to the input vector, and the other is non-orthogonal."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Orthogonal Partial Least Squares regression — OPLSR • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Orthogonal Partial Least Squares regression — OPLSR"><meta name="description" content="OPLS splits a data matrix into two parts. One part
+contains information orthogonal to the input vector, and the other is non-orthogonal."><meta property="og:description" content="OPLS splits a data matrix into two parts. One part
+contains information orthogonal to the input vector, and the other is non-orthogonal."></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
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     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
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+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Orthogonal Partial Least Squares regression</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/oplsr_class.R" class="external-link"><code>R/oplsr_class.R</code></a></small>
+
+      <h1>Orthogonal Partial Least Squares regression</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/oplsr_class.R" class="external-link"><code>R/oplsr_class.R</code></a></small>
       <div class="d-none name"><code>OPLSR.Rd</code></div>
     </div>
 
@@ -62,26 +50,26 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>number_components</dt>
+
+
+<dl><dt id="arg-number-components">number_components<a class="anchor" aria-label="anchor" href="#arg-number-components"></a></dt>
 <dd><p>(numeric, integer) The number of orthgonal components. The default is <code>2</code>.<br></p></dd>
 
 
-<dt>factor_name</dt>
+<dt id="arg-factor-name">factor_name<a class="anchor" aria-label="anchor" href="#arg-factor-name"></a></dt>
 <dd><p>(character) The name of a sample-meta column to use.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>OPLSR</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>opls_model</code></td><td>(list)</td></tr><tr><td><code>filtered</code></td><td>(DatasetExperiment)</td></tr><tr><td><code>orthogonal</code></td><td>(DatasetExperiment)</td></tr></table></div>
+    <p>A  <code>OPLSR</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>opls_model</code></td><td>(list)</td></tr><tr><td><code>filtered</code></td><td>(DatasetExperiment)</td></tr><tr><td><code>orthogonal</code></td><td>(DatasetExperiment)</td></tr></table></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>OPLSR</code> object inherits the following <code>struct</code> classes: <br><br><code>[OPLSR]</code> &gt;&gt; <code>[model]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -95,23 +83,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">OPLSR</span><span class="op">(</span><span class="st">'number_components'</span><span class="op">=</span><span class="fl">2</span>,factor_name<span class="op">=</span><span class="st">'Species'</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
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diff --git a/reference/PCA.html b/reference/PCA.html
index 3a156ff..923014f 100644
--- a/reference/PCA.html
+++ b/reference/PCA.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="PCA is a multivariate data reduction technique. It summarises the data in a smaller number of Principal Components that maximise variance."><title>Principal Component Analysis (PCA) — PCA • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Principal Component Analysis (PCA) — PCA"><meta property="og:description" content="PCA is a multivariate data reduction technique. It summarises the data in a smaller number of Principal Components that maximise variance."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Principal Component Analysis (PCA) — PCA • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Principal Component Analysis (PCA) — PCA"><meta name="description" content="PCA is a multivariate data reduction technique. It summarises the data in a smaller number of Principal Components that maximise variance."><meta property="og:description" content="PCA is a multivariate data reduction technique. It summarises the data in a smaller number of Principal Components that maximise variance."></head><body>
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     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
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+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Principal Component Analysis (PCA)</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/PCA_class.R" class="external-link"><code>R/PCA_class.R</code></a></small>
+
+      <h1>Principal Component Analysis (PCA)</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/PCA_class.R" class="external-link"><code>R/PCA_class.R</code></a></small>
       <div class="d-none name"><code>PCA.Rd</code></div>
     </div>
 
@@ -59,22 +47,22 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>number_components</dt>
+
+
+<dl><dt id="arg-number-components">number_components<a class="anchor" aria-label="anchor" href="#arg-number-components"></a></dt>
 <dd><p>(numeric, integer) The number of Principal Components calculated. The default is <code>2</code>.<br></p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>PCA</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>scores</code></td><td>(DatasetExperiment) A matrix of PCA scores where each column corresponds to a Principal Component.</td></tr><tr><td><code>loadings</code></td><td>(data.frame)</td></tr><tr><td><code>eigenvalues</code></td><td>(data.frame)</td></tr><tr><td><code>ssx</code></td><td>(numeric)</td></tr><tr><td><code>correlation</code></td><td>(data.frame)</td></tr><tr><td><code>that</code></td><td>(DatasetExperiment)</td></tr></table></div>
+    <p>A  <code>PCA</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>scores</code></td><td>(DatasetExperiment) A matrix of PCA scores where each column corresponds to a Principal Component.</td></tr><tr><td><code>loadings</code></td><td>(data.frame)</td></tr><tr><td><code>eigenvalues</code></td><td>(data.frame)</td></tr><tr><td><code>ssx</code></td><td>(numeric)</td></tr><tr><td><code>correlation</code></td><td>(data.frame)</td></tr><tr><td><code>that</code></td><td>(DatasetExperiment)</td></tr></table></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>PCA</code> object inherits the following <code>struct</code> classes: <br><br><code>[PCA]</code> &gt;&gt; <code>[model]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -86,23 +74,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
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-  
 
-  
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diff --git a/reference/PLSDA.html b/reference/PLSDA.html
index a620f90..8e7198d 100644
--- a/reference/PLSDA.html
+++ b/reference/PLSDA.html
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-      <img src="" class="logo" alt=""><h1>Partial least squares discriminant analysis</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/PLSDA_class.R" class="external-link"><code>R/PLSDA_class.R</code></a></small>
+
+      <h1>Partial least squares discriminant analysis</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/PLSDA_class.R" class="external-link"><code>R/PLSDA_class.R</code></a></small>
       <div class="d-none name"><code>PLSDA.Rd</code></div>
     </div>
 
@@ -59,36 +47,36 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>number_components</dt>
+
+
+<dl><dt id="arg-number-components">number_components<a class="anchor" aria-label="anchor" href="#arg-number-components"></a></dt>
 <dd><p>(numeric, integer) The number of PLS components. The default is <code>2</code>.<br></p></dd>
 
 
-<dt>factor_name</dt>
+<dt id="arg-factor-name">factor_name<a class="anchor" aria-label="anchor" href="#arg-factor-name"></a></dt>
 <dd><p>(character) The name of a sample-meta column to use.</p></dd>
 
 
-<dt>pred_method</dt>
+<dt id="arg-pred-method">pred_method<a class="anchor" aria-label="anchor" href="#arg-pred-method"></a></dt>
 <dd><p>(character) Prediction method. Allowed values are limited to the following:</p><ul><li><p><code>"max_yhat"</code>: The predicted group is selected based on the largest value of y_hat.</p></li>
 <li><p><code>"max_prob"</code>: The predicted group is selected based on the largest probability of group membership.</p></li>
 </ul><p>The default is <code>"max_prob"</code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>PLSDA</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>scores</code></td><td>(DatasetExperiment)</td></tr><tr><td><code>loadings</code></td><td>(data.frame)</td></tr><tr><td><code>yhat</code></td><td>(data.frame)</td></tr><tr><td><code>design_matrix</code></td><td>(data.frame)</td></tr><tr><td><code>y</code></td><td>(data.frame)</td></tr><tr><td><code>reg_coeff</code></td><td>(data.frame)</td></tr><tr><td><code>probability</code></td><td>(data.frame)</td></tr><tr><td><code>vip</code></td><td>(data.frame)</td></tr><tr><td><code>pls_model</code></td><td>(list)</td></tr><tr><td><code>pred</code></td><td>(data.frame)</td></tr><tr><td><code>threshold</code></td><td>(numeric)</td></tr><tr><td><code>sr</code></td><td>(data.frame) Selectivity ratio for a variable represents a measure of a variable's importance in the PLS model. The output data.frame contains a column of selectivity ratios, a column of p-values based on an F-distribution and a column indicating significance at p &lt; 0.05.</td></tr><tr><td><code>sr_pvalue</code></td><td>(data.frame) A p-value computed from the Selectivity Ratio based on an F-distribution.</td></tr></table></div>
+    <p>A  <code>PLSDA</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>scores</code></td><td>(DatasetExperiment)</td></tr><tr><td><code>loadings</code></td><td>(data.frame)</td></tr><tr><td><code>yhat</code></td><td>(data.frame)</td></tr><tr><td><code>design_matrix</code></td><td>(data.frame)</td></tr><tr><td><code>y</code></td><td>(data.frame)</td></tr><tr><td><code>reg_coeff</code></td><td>(data.frame)</td></tr><tr><td><code>probability</code></td><td>(data.frame)</td></tr><tr><td><code>vip</code></td><td>(data.frame)</td></tr><tr><td><code>pls_model</code></td><td>(list)</td></tr><tr><td><code>pred</code></td><td>(data.frame)</td></tr><tr><td><code>threshold</code></td><td>(numeric)</td></tr><tr><td><code>sr</code></td><td>(data.frame) Selectivity ratio for a variable represents a measure of a variable's importance in the PLS model. The output data.frame contains a column of selectivity ratios, a column of p-values based on an F-distribution and a column indicating significance at p &lt; 0.05.</td></tr><tr><td><code>sr_pvalue</code></td><td>(data.frame) A p-value computed from the Selectivity Ratio based on an F-distribution.</td></tr></table></div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>pls</code></p></li>
 </ul></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>PLSDA</code> object inherits the following <code>struct</code> classes: <br><br><code>[PLSDA]</code> &gt;&gt; <code>[PLSR]</code> &gt;&gt; <code>[model]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -115,23 +103,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">PLSDA</span><span class="op">(</span><span class="st">'number_components'</span><span class="op">=</span><span class="fl">2</span>,factor_name<span class="op">=</span><span class="st">'Species'</span><span class="op">)</span></span></span>
 </code></pre></div>
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Partial least squares regression</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/PLSR_class.R" class="external-link"><code>R/PLSR_class.R</code></a></small>
+
+      <h1>Partial least squares regression</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/PLSR_class.R" class="external-link"><code>R/PLSR_class.R</code></a></small>
       <div class="d-none name"><code>PLSR.Rd</code></div>
     </div>
 
@@ -59,30 +47,30 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>number_components</dt>
+
+
+<dl><dt id="arg-number-components">number_components<a class="anchor" aria-label="anchor" href="#arg-number-components"></a></dt>
 <dd><p>(numeric, integer) The number of PLS components. The default is <code>2</code>.<br></p></dd>
 
 
-<dt>factor_name</dt>
+<dt id="arg-factor-name">factor_name<a class="anchor" aria-label="anchor" href="#arg-factor-name"></a></dt>
 <dd><p>(character) The name of sample meta column(s) to use.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>PLSR</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>scores</code></td><td>(DatasetExperiment)</td></tr><tr><td><code>loadings</code></td><td>(data.frame)</td></tr><tr><td><code>yhat</code></td><td>(data.frame)</td></tr><tr><td><code>y</code></td><td>(data.frame)</td></tr><tr><td><code>reg_coeff</code></td><td>(data.frame)</td></tr><tr><td><code>vip</code></td><td>(data.frame)</td></tr><tr><td><code>pls_model</code></td><td>(list)</td></tr><tr><td><code>pred</code></td><td>(data.frame)</td></tr><tr><td><code>sr</code></td><td>(data.frame) Selectivity ratio for a variable represents a measure of a variable's importance in the PLS model. The output data.frame contains a column of selectivity ratios, a column of p-values based on an F-distribution and a column indicating significance at p &lt; 0.05.</td></tr><tr><td><code>sr_pvalue</code></td><td>(data.frame) A p-value computed from the Selectivity Ratio based on an F-distribution.</td></tr></table></div>
+    <p>A  <code>PLSR</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>scores</code></td><td>(DatasetExperiment)</td></tr><tr><td><code>loadings</code></td><td>(data.frame)</td></tr><tr><td><code>yhat</code></td><td>(data.frame)</td></tr><tr><td><code>y</code></td><td>(data.frame)</td></tr><tr><td><code>reg_coeff</code></td><td>(data.frame)</td></tr><tr><td><code>vip</code></td><td>(data.frame)</td></tr><tr><td><code>pls_model</code></td><td>(list)</td></tr><tr><td><code>pred</code></td><td>(data.frame)</td></tr><tr><td><code>sr</code></td><td>(data.frame) Selectivity ratio for a variable represents a measure of a variable's importance in the PLS model. The output data.frame contains a column of selectivity ratios, a column of p-values based on an F-distribution and a column indicating significance at p &lt; 0.05.</td></tr><tr><td><code>sr_pvalue</code></td><td>(data.frame) A p-value computed from the Selectivity Ratio based on an F-distribution.</td></tr></table></div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>pls</code></p></li>
 </ul></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>PLSR</code> object inherits the following <code>struct</code> classes: <br><br><code>[PLSR]</code> &gt;&gt; <code>[model]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -102,23 +90,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">PLSR</span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'run_order'</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
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diff --git a/reference/Rplot002.png b/reference/Rplot002.png
index 0b9b543..24b197a 100644
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diff --git a/reference/SVM.html b/reference/SVM.html
index bf0828e..617c74f 100644
--- a/reference/SVM.html
+++ b/reference/SVM.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Support Vector Machines (SVM) are a machine learning algorithm for classification. They can make use of kernel functions to generate highly non-linear boundaries between groups."><title>Support Vector Machine Classifier — SVM • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Support Vector Machine Classifier — SVM"><meta property="og:description" content="Support Vector Machines (SVM) are a machine learning algorithm for classification. They can make use of kernel functions to generate highly non-linear boundaries between groups."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-      <img src="" class="logo" alt=""><h1>Support Vector Machine Classifier</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/svm_classifier_class.R" class="external-link"><code>R/svm_classifier_class.R</code></a></small>
+
+      <h1>Support Vector Machine Classifier</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/svm_classifier_class.R" class="external-link"><code>R/svm_classifier_class.R</code></a></small>
       <div class="d-none name"><code>SVM.Rd</code></div>
     </div>
 
@@ -68,11 +56,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>factor_name</dt>
+
+
+<dl><dt id="arg-factor-name">factor_name<a class="anchor" aria-label="anchor" href="#arg-factor-name"></a></dt>
 <dd><p>(character) The name of a sample-meta column to use.</p></dd>
 
 
-<dt>kernel</dt>
+<dt id="arg-kernel">kernel<a class="anchor" aria-label="anchor" href="#arg-kernel"></a></dt>
 <dd><p>(character) Kernel type. Allowed values are limited to the following:</p><ul><li><p><code>"linear"</code>: .</p></li>
 <li><p><code>"polynomial"</code>: .</p></li>
 <li><p><code>"radial"</code>: .</p></li>
@@ -80,36 +70,34 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </ul><p>The default is <code>"linear"</code>.</p></dd>
 
 
-<dt>degree</dt>
+<dt id="arg-degree">degree<a class="anchor" aria-label="anchor" href="#arg-degree"></a></dt>
 <dd><p>(numeric) The polynomial degree. The default is <code>3</code>.<br></p></dd>
 
 
-<dt>gamma</dt>
+<dt id="arg-gamma">gamma<a class="anchor" aria-label="anchor" href="#arg-gamma"></a></dt>
 <dd><p>(numeric) The gamma parameter. The default is <code>1</code>.<br></p></dd>
 
 
-<dt>coef0</dt>
+<dt id="arg-coef-">coef0<a class="anchor" aria-label="anchor" href="#arg-coef-"></a></dt>
 <dd><p>(numeric) The offset coefficient. The default is <code>0</code>.<br></p></dd>
 
 
-<dt>cost</dt>
+<dt id="arg-cost">cost<a class="anchor" aria-label="anchor" href="#arg-cost"></a></dt>
 <dd><p>(numeric) The cost of violating the constraints. The default is <code>1</code>.<br></p></dd>
 
 
-<dt>class_weights</dt>
+<dt id="arg-class-weights">class_weights<a class="anchor" aria-label="anchor" href="#arg-class-weights"></a></dt>
 <dd><p>(numeric, character, NULL) A named vector of weights for the different classes.  Specifying
 "inverse" will choose the weights inversely proportional to the class distribution. The default is <code>NULL</code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>SVM</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>SV</code></td><td>(matrix)</td></tr><tr><td><code>index</code></td><td>(numeric)</td></tr><tr><td><code>coefs</code></td><td>(matrix)</td></tr><tr><td><code>pred</code></td><td>(data.frame)</td></tr><tr><td><code>decision_values</code></td><td>(data.frame)</td></tr></table><p>struct object</p>
+    <p>A  <code>SVM</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>SV</code></td><td>(matrix)</td></tr><tr><td><code>index</code></td><td>(numeric)</td></tr><tr><td><code>coefs</code></td><td>(matrix)</td></tr><tr><td><code>pred</code></td><td>(data.frame)</td></tr><tr><td><code>decision_values</code></td><td>(data.frame)</td></tr></table><p>struct object</p>
     </div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
@@ -117,7 +105,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 </ul></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>SVM</code> object inherits the following <code>struct</code> classes: <br><br><code>[SVM]</code> &gt;&gt; <code>[model]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -145,23 +133,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">SVM</span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'Species'</span>,gamma<span class="op">=</span><span class="fl">1</span><span class="op">)</span></span></span>
 </code></pre></div>
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diff --git a/reference/as_data_frame,filter_na_count-method.html b/reference/as_data_frame,filter_na_count-method.html
new file mode 100644
index 0000000..86658da
--- /dev/null
+++ b/reference/as_data_frame,filter_na_count-method.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://computational-metabolomics.github.io/structToolbox/reference/as_data_frame.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://computational-metabolomics.github.io/structToolbox/reference/as_data_frame.html">
+  </head>
+</html>
+
diff --git a/reference/as_data_frame,ttest-method.html b/reference/as_data_frame,ttest-method.html
new file mode 100644
index 0000000..86658da
--- /dev/null
+++ b/reference/as_data_frame,ttest-method.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://computational-metabolomics.github.io/structToolbox/reference/as_data_frame.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://computational-metabolomics.github.io/structToolbox/reference/as_data_frame.html">
+  </head>
+</html>
+
diff --git a/reference/as_data_frame,wilcox_test-method.html b/reference/as_data_frame,wilcox_test-method.html
new file mode 100644
index 0000000..86658da
--- /dev/null
+++ b/reference/as_data_frame,wilcox_test-method.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://computational-metabolomics.github.io/structToolbox/reference/as_data_frame.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://computational-metabolomics.github.io/structToolbox/reference/as_data_frame.html">
+  </head>
+</html>
+
diff --git a/reference/as_data_frame.html b/reference/as_data_frame.html
index 24614cc..b3d4201 100644
--- a/reference/as_data_frame.html
+++ b/reference/as_data_frame.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Convert the outputs of the input model into a data.frame."><title>Convert to data.frame — as_data_frame • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Convert to data.frame — as_data_frame"><meta property="og:description" content="Convert the outputs of the input model into a data.frame."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.17.3</small>
+
 
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   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Convert to data.frame</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/as_data_frame_doc.R" class="external-link"><code>R/as_data_frame_doc.R</code></a>, <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/filter_na_count.R" class="external-link"><code>R/filter_na_count.R</code></a>, <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/ttest_class.R" class="external-link"><code>R/ttest_class.R</code></a>, and 1 more</small>
+
+      <h1>Convert to data.frame</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/as_data_frame_doc.R" class="external-link"><code>R/as_data_frame_doc.R</code></a>, <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/filter_na_count.R" class="external-link"><code>R/filter_na_count.R</code></a>, <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/ttest_class.R" class="external-link"><code>R/ttest_class.R</code></a>, and 1 more</small>
       <div class="d-none name"><code>as_data_frame.Rd</code></div>
     </div>
 
@@ -54,27 +42,27 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S4 method for filter_na_count</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S4 method for class 'filter_na_count'</span></span>
 <span><span class="fu">as_data_frame</span><span class="op">(</span><span class="va">M</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for ttest</span></span>
+<span><span class="co"># S4 method for class 'ttest'</span></span>
 <span><span class="fu">as_data_frame</span><span class="op">(</span><span class="va">M</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for wilcox_test</span></span>
+<span><span class="co"># S4 method for class 'wilcox_test'</span></span>
 <span><span class="fu">as_data_frame</span><span class="op">(</span><span class="va">M</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>M</dt>
+
+
+<dl><dt id="arg-m">M<a class="anchor" aria-label="anchor" href="#arg-m"></a></dt>
 <dd><p>a model object</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A data.frame of model outputs</p>
+    <p>A data.frame of model outputs</p>
     </div>
 
     <div class="section level2">
@@ -85,23 +73,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">df</span> <span class="op">=</span> <span class="fu">as_data_frame</span><span class="op">(</span><span class="va">M</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
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diff --git a/reference/autoscale.html b/reference/autoscale.html
index 71c8075..4382865 100644
--- a/reference/autoscale.html
+++ b/reference/autoscale.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Each variable/feature is mean centred and scaled by the standard deviation. The transformed variables have zero-mean and unit-variance."><title>Autoscaling — autoscale • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Autoscaling — autoscale"><meta property="og:description" content="Each variable/feature is mean centred and scaled by the standard deviation. The transformed variables have zero-mean and unit-variance."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-  <a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="github">
-    <span class="fab fa fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Autoscaling</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/autoscale_class.R" class="external-link"><code>R/autoscale_class.R</code></a></small>
+
+      <h1>Autoscaling</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/autoscale_class.R" class="external-link"><code>R/autoscale_class.R</code></a></small>
       <div class="d-none name"><code>autoscale.Rd</code></div>
     </div>
 
@@ -59,25 +47,25 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>mode</dt>
+
+
+<dl><dt id="arg-mode">mode<a class="anchor" aria-label="anchor" href="#arg-mode"></a></dt>
 <dd><p>(character) Mode of action. Allowed values are limited to the following:</p><ul><li><p><code>"data"</code>: Autoscaling is applied to the data matrix only.</p></li>
 <li><p><code>"sample_meta"</code>: Autoscaling is applied to the sample_meta data only.</p></li>
 <li><p><code>"both"</code>: Autoscaling is applied to both the data matrix and the meta data.</p></li>
 </ul><p>The default is <code>"data"</code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>autoscale</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>autoscaled</code></td><td>(DatasetExperiment)</td></tr><tr><td><code>mean_data</code></td><td>(numeric)</td></tr><tr><td><code>sd_data</code></td><td>(numeric)</td></tr><tr><td><code>mean_sample_meta</code></td><td>(numeric)</td></tr><tr><td><code>sd_sample_meta</code></td><td>(numeric)</td></tr></table></div>
+    <p>A  <code>autoscale</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>autoscaled</code></td><td>(DatasetExperiment)</td></tr><tr><td><code>mean_data</code></td><td>(numeric)</td></tr><tr><td><code>sd_data</code></td><td>(numeric)</td></tr><tr><td><code>mean_sample_meta</code></td><td>(numeric)</td></tr><tr><td><code>sd_sample_meta</code></td><td>(numeric)</td></tr></table></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>autoscale</code> object inherits the following <code>struct</code> classes: <br><br><code>[autoscale]</code> &gt;&gt; <code>[model]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -93,23 +81,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="model_predict.html">model_predict</a></span><span class="op">(</span><span class="va">M</span>,<span class="va">D</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
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   </body></html>
 
diff --git a/reference/balanced_accuracy.html b/reference/balanced_accuracy.html
index 86d6de7..4db2bc9 100644
--- a/reference/balanced_accuracy.html
+++ b/reference/balanced_accuracy.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Balanced Accuracy is the average proportion of correctly classified samples across all groups."><title>Balanced Accuracy — balanced_accuracy • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Balanced Accuracy — balanced_accuracy"><meta property="og:description" content="Balanced Accuracy is the average proportion of correctly classified samples across all groups."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
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-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Balanced Accuracy — balanced_accuracy • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Balanced Accuracy — balanced_accuracy"><meta name="description" content="Balanced Accuracy is the average proportion of correctly classified samples across all groups."><meta property="og:description" content="Balanced Accuracy is the average proportion of correctly classified samples across all groups."></head><body>
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+
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.17.3</small>
+
 
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-  <a class="nav-link" href="../reference/index.html">Reference</a>
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
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+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
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-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Balanced Accuracy</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/balanced_accuracy_class.R" class="external-link"><code>R/balanced_accuracy_class.R</code></a></small>
+
+      <h1>Balanced Accuracy</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/balanced_accuracy_class.R" class="external-link"><code>R/balanced_accuracy_class.R</code></a></small>
       <div class="d-none name"><code>balanced_accuracy.Rd</code></div>
     </div>
 
@@ -59,21 +47,21 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>...</dt>
+
+
+<dl><dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>
+    <p>A  <code>
 balanced_accuracy
 </code> object. This object has no <code>output</code> slots.</p>
     </div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>balanced_accuracy</code> object inherits the following <code>struct</code> classes: <br><br><code>[balanced_accuracy]</code> &gt;&gt; <code>[metric]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -89,23 +77,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">XCV</span> <span class="op">=</span> <span class="fu"><a href="run.html">run</a></span><span class="op">(</span><span class="va">XCV</span>,<span class="va">D</span>,<span class="va">MET</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/blank_filter.html b/reference/blank_filter.html
index 468b2a5..d8076f7 100644
--- a/reference/blank_filter.html
+++ b/reference/blank_filter.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="A blank filter filters features by comparing the median intensity of blank samples to the median intensity of samples. Features where the relative intensity (fold change) is not large when compared to the blank are removed. The number of times a feature is detected across all blank samples may also be considered. If the feature is not detected in a high enough proportion of the blanks then it is not removed."><title>Blank filter — blank_filter • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Blank filter — blank_filter"><meta property="og:description" content="A blank filter filters features by comparing the median intensity of blank samples to the median intensity of samples. Features where the relative intensity (fold change) is not large when compared to the blank are removed. The number of times a feature is detected across all blank samples may also be considered. If the feature is not detected in a high enough proportion of the blanks then it is not removed."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Blank filter — blank_filter • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Blank filter — blank_filter"><meta name="description" content="A blank filter filters features by comparing the median intensity of blank samples to the median intensity of samples. Features where the relative intensity (fold change) is not large when compared to the blank are removed. The number of times a feature is detected across all blank samples may also be considered. If the feature is not detected in a high enough proportion of the blanks then it is not removed."><meta property="og:description" content="A blank filter filters features by comparing the median intensity of blank samples to the median intensity of samples. Features where the relative intensity (fold change) is not large when compared to the blank are removed. The number of times a feature is detected across all blank samples may also be considered. If the feature is not detected in a high enough proportion of the blanks then it is not removed."></head><body>
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-      <img src="" class="logo" alt=""><h1>Blank filter</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/blank_filter_class.R" class="external-link"><code>R/blank_filter_class.R</code></a></small>
+
+      <h1>Blank filter</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/blank_filter_class.R" class="external-link"><code>R/blank_filter_class.R</code></a></small>
       <div class="d-none name"><code>blank_filter.Rd</code></div>
     </div>
 
@@ -66,51 +54,50 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>fold_change</dt>
+
+
+<dl><dt id="arg-fold-change">fold_change<a class="anchor" aria-label="anchor" href="#arg-fold-change"></a></dt>
 <dd><p>(numeric) Features with fold change less than this value are removed. The default is <code>20</code>.<br></p></dd>
 
 
-<dt>blank_label</dt>
+<dt id="arg-blank-label">blank_label<a class="anchor" aria-label="anchor" href="#arg-blank-label"></a></dt>
 <dd><p>(character) The label used to identify blank samples. The default is <code>"blank"</code>.</p></dd>
 
 
-<dt>qc_label</dt>
+<dt id="arg-qc-label">qc_label<a class="anchor" aria-label="anchor" href="#arg-qc-label"></a></dt>
 <dd><p>(character, NULL) The label used to identify QC samples. If set to NULL then the median of the samples is used. The default is <code>"QC"</code>.</p></dd>
 
 
-<dt>factor_name</dt>
+<dt id="arg-factor-name">factor_name<a class="anchor" aria-label="anchor" href="#arg-factor-name"></a></dt>
 <dd><p>(character) The name of a sample-meta column to use.</p></dd>
 
 
-<dt>fraction_in_blank</dt>
+<dt id="arg-fraction-in-blank">fraction_in_blank<a class="anchor" aria-label="anchor" href="#arg-fraction-in-blank"></a></dt>
 <dd><p>(numeric) Features present in less than this proportion of the blanks are not considered for removal. The default is <code>0</code>.<br></p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>blank_filter</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>filtered</code></td><td>(DatasetExperiment) A DatasetExperiment object containing the filtered data.</td></tr><tr><td><code>flags</code></td><td>(data.frame) A flag indicating whether the feature was rejected  or not.</td></tr></table></div>
+    <p>A  <code>blank_filter</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>filtered</code></td><td>(DatasetExperiment) A DatasetExperiment object containing the filtered data.</td></tr><tr><td><code>flags</code></td><td>(data.frame) A flag indicating whether the feature was rejected  or not.</td></tr></table></div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>pmp</code></p></li>
 </ul></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>blank_filter</code> object inherits the following <code>struct</code> classes: <br><br><code>[blank_filter]</code> &gt;&gt; <code>[model]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
     <div class="section level2">
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
-    <p>Jankevics A, Lloyd GR, Weber RJM (2023). <em>pmp: Peak Matrix Processing
-and signal batch correction for metabolomics datasets</em>.
-doi:10.18129/B9.bioc.pmp <a href="https://doi.org/10.18129/B9.bioc.pmp" class="external-link">https://doi.org/10.18129/B9.bioc.pmp</a>, R
-package version 1.14.0, <a href="https://bioconductor.org/packages/pmp" class="external-link">https://bioconductor.org/packages/pmp</a>.</p>
+    <p>Jankevics A, Lloyd GR, Weber RJM (????). <em>pmp: Peak Matrix Processing
+and signal batch correction for metabolomics datasets</em>. R package
+version 1.15.1.</p>
     </div>
 
     <div class="section level2">
@@ -130,23 +117,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="model_apply.html">model_apply</a></span><span class="op">(</span><span class="va">M</span>,<span class="va">D</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
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diff --git a/reference/blank_filter_hist.html b/reference/blank_filter_hist.html
index 252943e..e9fae28 100644
--- a/reference/blank_filter_hist.html
+++ b/reference/blank_filter_hist.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Histogram of blank filter fold changes — blank_filter_hist • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Histogram of blank filter fold changes — blank_filter_hist"><meta name="description" content="A histogram of the calculated fold changes for the blank filter (median samples divided by median blanks)"><meta property="og:description" content="A histogram of the calculated fold changes for the blank filter (median samples divided by median blanks)"></head><body>
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
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+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Histogram of blank filter fold changes</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/blank_filter_class.R" class="external-link"><code>R/blank_filter_class.R</code></a></small>
+
+      <h1>Histogram of blank filter fold changes</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/blank_filter_class.R" class="external-link"><code>R/blank_filter_class.R</code></a></small>
       <div class="d-none name"><code>blank_filter_hist.Rd</code></div>
     </div>
 
@@ -59,22 +47,22 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>...</dt>
+
+
+<dl><dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>
+    <p>A  <code>
 blank_filter_hist
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>blank_filter_hist</code> object inherits the following <code>struct</code> classes: <br><br><code>[blank_filter_hist]</code> &gt;&gt; <code>[chart]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -86,23 +74,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">blank_filter_hist</span><span class="op">(</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
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diff --git a/reference/bootstrap.html b/reference/bootstrap.html
index 8e4912e..a28c8e1 100644
--- a/reference/bootstrap.html
+++ b/reference/bootstrap.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="In bootstrap resampling a subset of samples is selected at random with replacement to form a training set. Any sample not selected for training is included in the test set. This process is repeated many times, and performance metrics are computed for each repetition."><title>Bootstrap resampling — bootstrap • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Bootstrap resampling — bootstrap"><meta property="og:description" content="In bootstrap resampling a subset of samples is selected at random with replacement to form a training set. Any sample not selected for training is included in the test set. This process is repeated many times, and performance metrics are computed for each repetition."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Bootstrap resampling — bootstrap • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Bootstrap resampling — bootstrap"><meta name="description" content="In bootstrap resampling a subset of samples is selected at random with replacement to form a training set. Any sample not selected for training is included in the test set. This process is repeated many times, and performance metrics are computed for each repetition."><meta property="og:description" content="In bootstrap resampling a subset of samples is selected at random with replacement to form a training set. Any sample not selected for training is included in the test set. This process is repeated many times, and performance metrics are computed for each repetition."></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary"><div class="container">
-    
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.17.3</small>
+
 
-    
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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     <div id="navbar" class="collapse navbar-collapse ms-3">
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-  <a class="nav-link" href="../reference/index.html">Reference</a>
-</li>
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
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-     
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-</li>
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
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+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Bootstrap resampling</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/bootstrap_class.R" class="external-link"><code>R/bootstrap_class.R</code></a></small>
+
+      <h1>Bootstrap resampling</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/bootstrap_class.R" class="external-link"><code>R/bootstrap_class.R</code></a></small>
       <div class="d-none name"><code>bootstrap.Rd</code></div>
     </div>
 
@@ -59,26 +47,26 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>number_of_repetitions</dt>
+
+
+<dl><dt id="arg-number-of-repetitions">number_of_repetitions<a class="anchor" aria-label="anchor" href="#arg-number-of-repetitions"></a></dt>
 <dd><p>(numeric, integer) The number of bootstrap repetitions. The default is <code>100</code>.<br></p></dd>
 
 
-<dt>collect</dt>
+<dt id="arg-collect">collect<a class="anchor" aria-label="anchor" href="#arg-collect"></a></dt>
 <dd><p>(character) The name of a model output to collect over all bootstrap repetitions, in addition to the input metric.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>bootstrap</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>results</code></td><td>(data.frame)</td></tr><tr><td><code>metric</code></td><td>(data.frame)</td></tr><tr><td><code>collected</code></td><td>(logical, list)</td></tr></table></div>
+    <p>A  <code>bootstrap</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>results</code></td><td>(data.frame)</td></tr><tr><td><code>metric</code></td><td>(data.frame)</td></tr><tr><td><code>collected</code></td><td>(logical, list)</td></tr></table></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>bootstrap</code> object inherits the following <code>struct</code> classes: <br><br><code>[bootstrap]</code> &gt;&gt; <code>[resampler]</code> &gt;&gt; <code>[iterator]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -92,23 +80,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">I</span> <span class="op">=</span> <span class="fu">bootstrap</span><span class="op">(</span>number_of_repetitions <span class="op">=</span> <span class="fl">10</span>, collect <span class="op">=</span> <span class="st">'vip'</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/calculate,balanced_accuracy-method.html b/reference/calculate,balanced_accuracy-method.html
new file mode 100644
index 0000000..a1695e3
--- /dev/null
+++ b/reference/calculate,balanced_accuracy-method.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://computational-metabolomics.github.io/structToolbox/reference/calculate.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://computational-metabolomics.github.io/structToolbox/reference/calculate.html">
+  </head>
+</html>
+
diff --git a/reference/calculate,r_squared-method.html b/reference/calculate,r_squared-method.html
new file mode 100644
index 0000000..a1695e3
--- /dev/null
+++ b/reference/calculate,r_squared-method.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://computational-metabolomics.github.io/structToolbox/reference/calculate.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://computational-metabolomics.github.io/structToolbox/reference/calculate.html">
+  </head>
+</html>
+
diff --git a/reference/calculate.html b/reference/calculate.html
index f66893f..1d004e6 100644
--- a/reference/calculate.html
+++ b/reference/calculate.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Calculate metric"><title>Calculate metric — calculate,AUC-method • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Calculate metric — calculate,AUC-method"><meta property="og:description" content="Calculate metric"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
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-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Calculate metric — calculate,AUC-method • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Calculate metric — calculate,AUC-method"><meta name="description" content="Calculate metric"><meta property="og:description" content="Calculate metric"></head><body>
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-    
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary"><div class="container">
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+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.17.3</small>
+
 
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Calculate metric</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/AUC_metric_class.R" class="external-link"><code>R/AUC_metric_class.R</code></a>, <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/balanced_accuracy_class.R" class="external-link"><code>R/balanced_accuracy_class.R</code></a>, <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/calculate_doc.R" class="external-link"><code>R/calculate_doc.R</code></a>, and 1 more</small>
+
+      <h1>Calculate metric</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/AUC_metric_class.R" class="external-link"><code>R/AUC_metric_class.R</code></a>, <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/balanced_accuracy_class.R" class="external-link"><code>R/balanced_accuracy_class.R</code></a>, <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/calculate_doc.R" class="external-link"><code>R/calculate_doc.R</code></a>, and 1 more</small>
       <div class="d-none name"><code>calculate.Rd</code></div>
     </div>
 
@@ -54,35 +42,35 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S4 method for AUC</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S4 method for class 'AUC'</span></span>
 <span><span class="fu">calculate</span><span class="op">(</span><span class="va">obj</span>, <span class="va">Y</span>, <span class="va">Yhat</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for balanced_accuracy</span></span>
+<span><span class="co"># S4 method for class 'balanced_accuracy'</span></span>
 <span><span class="fu">calculate</span><span class="op">(</span><span class="va">obj</span>, <span class="va">Y</span>, <span class="va">Yhat</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for r_squared</span></span>
+<span><span class="co"># S4 method for class 'r_squared'</span></span>
 <span><span class="fu">calculate</span><span class="op">(</span><span class="va">obj</span>, <span class="va">Y</span>, <span class="va">Yhat</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>obj</dt>
+
+
+<dl><dt id="arg-obj">obj<a class="anchor" aria-label="anchor" href="#arg-obj"></a></dt>
 <dd><p>a metric object</p></dd>
 
 
-<dt>Y</dt>
+<dt id="arg-y">Y<a class="anchor" aria-label="anchor" href="#arg-y"></a></dt>
 <dd><p>the true values/group labels</p></dd>
 
 
-<dt>Yhat</dt>
+<dt id="arg-yhat">Yhat<a class="anchor" aria-label="anchor" href="#arg-yhat"></a></dt>
 <dd><p>the predicted values/group labels</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>a modified metric object</p>
+    <p>a modified metric object</p>
     </div>
 
     <div class="section level2">
@@ -98,23 +86,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/chart_plot,DatasetExperiment_boxplot,DatasetExperiment-method.html b/reference/chart_plot,DatasetExperiment_boxplot,DatasetExperiment-method.html
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+++ b/reference/chart_plot,DatasetExperiment_factor_boxplot,DatasetExperiment-method.html
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diff --git a/reference/chart_plot,DatasetExperiment_heatmap,DatasetExperiment-method.html b/reference/chart_plot,DatasetExperiment_heatmap,DatasetExperiment-method.html
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-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
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+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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-    
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   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>chart_plot method</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/DFA_class.R" class="external-link"><code>R/DFA_class.R</code></a>, <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/scatter_chart_class.R" class="external-link"><code>R/scatter_chart_class.R</code></a>, <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/PCA_plotfcns.R" class="external-link"><code>R/PCA_plotfcns.R</code></a>, and 28 more</small>
+
+      <h1>chart_plot method</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/DFA_class.R" class="external-link"><code>R/DFA_class.R</code></a>, <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/scatter_chart_class.R" class="external-link"><code>R/scatter_chart_class.R</code></a>, <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/PCA_plotfcns.R" class="external-link"><code>R/PCA_plotfcns.R</code></a>, and 28 more</small>
       <div class="d-none name"><code>chart_plot.Rd</code></div>
     </div>
 
@@ -54,180 +42,180 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S4 method for dfa_scores_plot,DFA</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S4 method for class 'dfa_scores_plot,DFA'</span></span>
 <span><span class="fu">chart_plot</span><span class="op">(</span><span class="va">obj</span>, <span class="va">dobj</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for scatter_chart,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'scatter_chart,DatasetExperiment'</span></span>
 <span><span class="fu">chart_plot</span><span class="op">(</span><span class="va">obj</span>, <span class="va">dobj</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for pca_correlation_plot,PCA</span></span>
+<span><span class="co"># S4 method for class 'pca_correlation_plot,PCA'</span></span>
 <span><span class="fu">chart_plot</span><span class="op">(</span><span class="va">obj</span>, <span class="va">dobj</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for pca_scores_plot,PCA</span></span>
+<span><span class="co"># S4 method for class 'pca_scores_plot,PCA'</span></span>
 <span><span class="fu">chart_plot</span><span class="op">(</span><span class="va">obj</span>, <span class="va">dobj</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for pca_biplot,PCA</span></span>
+<span><span class="co"># S4 method for class 'pca_biplot,PCA'</span></span>
 <span><span class="fu">chart_plot</span><span class="op">(</span><span class="va">obj</span>, <span class="va">dobj</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for pca_loadings_plot,PCA</span></span>
+<span><span class="co"># S4 method for class 'pca_loadings_plot,PCA'</span></span>
 <span><span class="fu">chart_plot</span><span class="op">(</span><span class="va">obj</span>, <span class="va">dobj</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for pca_scree_plot,PCA</span></span>
+<span><span class="co"># S4 method for class 'pca_scree_plot,PCA'</span></span>
 <span><span class="fu">chart_plot</span><span class="op">(</span><span class="va">obj</span>, <span class="va">dobj</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for pca_dstat_plot,PCA</span></span>
+<span><span class="co"># S4 method for class 'pca_dstat_plot,PCA'</span></span>
 <span><span class="fu">chart_plot</span><span class="op">(</span><span class="va">obj</span>, <span class="va">dobj</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for plsr_prediction_plot,PLSR</span></span>
+<span><span class="co"># S4 method for class 'plsr_prediction_plot,PLSR'</span></span>
 <span><span class="fu">chart_plot</span><span class="op">(</span><span class="va">obj</span>, <span class="va">dobj</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for plsr_residual_hist,PLSR</span></span>
+<span><span class="co"># S4 method for class 'plsr_residual_hist,PLSR'</span></span>
 <span><span class="fu">chart_plot</span><span class="op">(</span><span class="va">obj</span>, <span class="va">dobj</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for plsr_qq_plot,PLSR</span></span>
+<span><span class="co"># S4 method for class 'plsr_qq_plot,PLSR'</span></span>
 <span><span class="fu">chart_plot</span><span class="op">(</span><span class="va">obj</span>, <span class="va">dobj</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for plsr_cook_dist,PLSR</span></span>
+<span><span class="co"># S4 method for class 'plsr_cook_dist,PLSR'</span></span>
 <span><span class="fu">chart_plot</span><span class="op">(</span><span class="va">obj</span>, <span class="va">dobj</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for pls_scores_plot,PLSR</span></span>
+<span><span class="co"># S4 method for class 'pls_scores_plot,PLSR'</span></span>
 <span><span class="fu">chart_plot</span><span class="op">(</span><span class="va">obj</span>, <span class="va">dobj</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for plsda_predicted_plot,PLSDA</span></span>
+<span><span class="co"># S4 method for class 'plsda_predicted_plot,PLSDA'</span></span>
 <span><span class="fu">chart_plot</span><span class="op">(</span><span class="va">obj</span>, <span class="va">dobj</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for plsda_roc_plot,PLSDA</span></span>
+<span><span class="co"># S4 method for class 'plsda_roc_plot,PLSDA'</span></span>
 <span><span class="fu">chart_plot</span><span class="op">(</span><span class="va">obj</span>, <span class="va">dobj</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for pls_vip_plot,PLSR</span></span>
+<span><span class="co"># S4 method for class 'pls_vip_plot,PLSR'</span></span>
 <span><span class="fu">chart_plot</span><span class="op">(</span><span class="va">obj</span>, <span class="va">dobj</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for pls_regcoeff_plot,PLSR</span></span>
+<span><span class="co"># S4 method for class 'pls_regcoeff_plot,PLSR'</span></span>
 <span><span class="fu">chart_plot</span><span class="op">(</span><span class="va">obj</span>, <span class="va">dobj</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for blank_filter_hist,blank_filter</span></span>
+<span><span class="co"># S4 method for class 'blank_filter_hist,blank_filter'</span></span>
 <span><span class="fu">chart_plot</span><span class="op">(</span><span class="va">obj</span>, <span class="va">dobj</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for confounders_lsq_barchart,confounders_clsq</span></span>
+<span><span class="co"># S4 method for class 'confounders_lsq_barchart,confounders_clsq'</span></span>
 <span><span class="fu">chart_plot</span><span class="op">(</span><span class="va">obj</span>, <span class="va">dobj</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for confounders_lsq_boxplot,confounders_clsq</span></span>
+<span><span class="co"># S4 method for class 'confounders_lsq_boxplot,confounders_clsq'</span></span>
 <span><span class="fu">chart_plot</span><span class="op">(</span><span class="va">obj</span>, <span class="va">dobj</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for feature_boxplot,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'feature_boxplot,DatasetExperiment'</span></span>
 <span><span class="fu">chart_plot</span><span class="op">(</span><span class="va">obj</span>, <span class="va">dobj</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for mv_histogram,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'mv_histogram,DatasetExperiment'</span></span>
 <span><span class="fu">chart_plot</span><span class="op">(</span><span class="va">obj</span>, <span class="va">dobj</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for mv_boxplot,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'mv_boxplot,DatasetExperiment'</span></span>
 <span><span class="fu">chart_plot</span><span class="op">(</span><span class="va">obj</span>, <span class="va">dobj</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for DatasetExperiment_dist,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'DatasetExperiment_dist,DatasetExperiment'</span></span>
 <span><span class="fu">chart_plot</span><span class="op">(</span><span class="va">obj</span>, <span class="va">dobj</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for DatasetExperiment_boxplot,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'DatasetExperiment_boxplot,DatasetExperiment'</span></span>
 <span><span class="fu">chart_plot</span><span class="op">(</span><span class="va">obj</span>, <span class="va">dobj</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for compare_dist,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'compare_dist,DatasetExperiment'</span></span>
 <span><span class="fu">chart_plot</span><span class="op">(</span><span class="va">obj</span>, <span class="va">dobj</span>, <span class="va">eobj</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for DatasetExperiment_heatmap,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'DatasetExperiment_heatmap,DatasetExperiment'</span></span>
 <span><span class="fu">chart_plot</span><span class="op">(</span><span class="va">obj</span>, <span class="va">dobj</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for DatasetExperiment_factor_boxplot,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'DatasetExperiment_factor_boxplot,DatasetExperiment'</span></span>
 <span><span class="fu">chart_plot</span><span class="op">(</span><span class="va">obj</span>, <span class="va">dobj</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for feature_profile_array,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'feature_profile_array,DatasetExperiment'</span></span>
 <span><span class="fu">chart_plot</span><span class="op">(</span><span class="va">obj</span>, <span class="va">dobj</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for feature_profile,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'feature_profile,DatasetExperiment'</span></span>
 <span><span class="fu">chart_plot</span><span class="op">(</span><span class="va">obj</span>, <span class="va">dobj</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for fold_change_plot,fold_change</span></span>
+<span><span class="co"># S4 method for class 'fold_change_plot,fold_change'</span></span>
 <span><span class="fu">chart_plot</span><span class="op">(</span><span class="va">obj</span>, <span class="va">dobj</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for fs_line,forward_selection_by_rank</span></span>
+<span><span class="co"># S4 method for class 'fs_line,forward_selection_by_rank'</span></span>
 <span><span class="fu">chart_plot</span><span class="op">(</span><span class="va">obj</span>, <span class="va">dobj</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for glog_opt_plot,glog_transform</span></span>
+<span><span class="co"># S4 method for class 'glog_opt_plot,glog_transform'</span></span>
 <span><span class="fu">chart_plot</span><span class="op">(</span><span class="va">obj</span>, <span class="va">dobj</span>, <span class="va">gobj</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for gs_line,grid_search_1d</span></span>
+<span><span class="co"># S4 method for class 'gs_line,grid_search_1d'</span></span>
 <span><span class="fu">chart_plot</span><span class="op">(</span><span class="va">obj</span>, <span class="va">dobj</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for hca_dendrogram,HCA</span></span>
+<span><span class="co"># S4 method for class 'hca_dendrogram,HCA'</span></span>
 <span><span class="fu">chart_plot</span><span class="op">(</span><span class="va">obj</span>, <span class="va">dobj</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for kfoldxcv_grid,kfold_xval</span></span>
+<span><span class="co"># S4 method for class 'kfoldxcv_grid,kfold_xval'</span></span>
 <span><span class="fu">chart_plot</span><span class="op">(</span><span class="va">obj</span>, <span class="va">dobj</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for kfoldxcv_metric,kfold_xval</span></span>
+<span><span class="co"># S4 method for class 'kfoldxcv_metric,kfold_xval'</span></span>
 <span><span class="fu">chart_plot</span><span class="op">(</span><span class="va">obj</span>, <span class="va">dobj</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for kw_p_hist,kw_rank_sum</span></span>
+<span><span class="co"># S4 method for class 'kw_p_hist,kw_rank_sum'</span></span>
 <span><span class="fu">chart_plot</span><span class="op">(</span><span class="va">obj</span>, <span class="va">dobj</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for mv_feature_filter_hist,mv_feature_filter</span></span>
+<span><span class="co"># S4 method for class 'mv_feature_filter_hist,mv_feature_filter'</span></span>
 <span><span class="fu">chart_plot</span><span class="op">(</span><span class="va">obj</span>, <span class="va">dobj</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for mv_sample_filter_hist,mv_sample_filter</span></span>
+<span><span class="co"># S4 method for class 'mv_sample_filter_hist,mv_sample_filter'</span></span>
 <span><span class="fu">chart_plot</span><span class="op">(</span><span class="va">obj</span>, <span class="va">dobj</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for permutation_test_plot,permutation_test</span></span>
+<span><span class="co"># S4 method for class 'permutation_test_plot,permutation_test'</span></span>
 <span><span class="fu">chart_plot</span><span class="op">(</span><span class="va">obj</span>, <span class="va">dobj</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for plsda_feature_importance_plot,PLSDA</span></span>
+<span><span class="co"># S4 method for class 'plsda_feature_importance_plot,PLSDA'</span></span>
 <span><span class="fu">chart_plot</span><span class="op">(</span><span class="va">obj</span>, <span class="va">dobj</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for pqn_norm_hist,pqn_norm</span></span>
+<span><span class="co"># S4 method for class 'pqn_norm_hist,pqn_norm'</span></span>
 <span><span class="fu">chart_plot</span><span class="op">(</span><span class="va">obj</span>, <span class="va">dobj</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for resample_chart,resample</span></span>
+<span><span class="co"># S4 method for class 'resample_chart,resample'</span></span>
 <span><span class="fu">chart_plot</span><span class="op">(</span><span class="va">obj</span>, <span class="va">dobj</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for rsd_filter_hist,rsd_filter</span></span>
+<span><span class="co"># S4 method for class 'rsd_filter_hist,rsd_filter'</span></span>
 <span><span class="fu">chart_plot</span><span class="op">(</span><span class="va">obj</span>, <span class="va">dobj</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for feature_profile,sb_corr</span></span>
+<span><span class="co"># S4 method for class 'feature_profile,sb_corr'</span></span>
 <span><span class="fu">chart_plot</span><span class="op">(</span><span class="va">obj</span>, <span class="va">dobj</span>, <span class="va">gobj</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for svm_plot_2d,SVM</span></span>
+<span><span class="co"># S4 method for class 'svm_plot_2d,SVM'</span></span>
 <span><span class="fu">chart_plot</span><span class="op">(</span><span class="va">obj</span>, <span class="va">dobj</span>, <span class="va">gobj</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for tSNE_scatter,tSNE</span></span>
+<span><span class="co"># S4 method for class 'tSNE_scatter,tSNE'</span></span>
 <span><span class="fu">chart_plot</span><span class="op">(</span><span class="va">obj</span>, <span class="va">dobj</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for tic_chart,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'tic_chart,DatasetExperiment'</span></span>
 <span><span class="fu">chart_plot</span><span class="op">(</span><span class="va">obj</span>, <span class="va">dobj</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for wilcox_p_hist,wilcox_test</span></span>
+<span><span class="co"># S4 method for class 'wilcox_p_hist,wilcox_test'</span></span>
 <span><span class="fu">chart_plot</span><span class="op">(</span><span class="va">obj</span>, <span class="va">dobj</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>obj</dt>
+
+
+<dl><dt id="arg-obj">obj<a class="anchor" aria-label="anchor" href="#arg-obj"></a></dt>
 <dd><p>a chart object</p></dd>
 
 
-<dt>dobj</dt>
+<dt id="arg-dobj">dobj<a class="anchor" aria-label="anchor" href="#arg-dobj"></a></dt>
 <dd><p>a struct object</p></dd>
 
 
-<dt>eobj</dt>
+<dt id="arg-eobj">eobj<a class="anchor" aria-label="anchor" href="#arg-eobj"></a></dt>
 <dd><p>a second DatasetExperiment object to compare with the first</p></dd>
 
 
-<dt>gobj</dt>
+<dt id="arg-gobj">gobj<a class="anchor" aria-label="anchor" href="#arg-gobj"></a></dt>
 <dd><p>The DatasetExperiment object before signal correction was applied.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>a plot object</p>
+    <p>a plot object</p>
     </div>
 
     <div class="section level2">
@@ -241,23 +229,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> description:   </span>
 </code></pre></div>
     </div>
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diff --git a/reference/classical_lsq.html b/reference/classical_lsq.html
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--- a/reference/classical_lsq.html
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@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="In univariate classical least squares regression a line is fitted between each feature/variable and a response variable. The fitted line minimises the sum of squared differences between the true response and the predicted response. The coefficients (offset, gradient) of the fit can be tested for significance."><title>Univariate Classical Least Squares Regression — classical_lsq • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Univariate Classical Least Squares Regression — classical_lsq"><meta property="og:description" content="In univariate classical least squares regression a line is fitted between each feature/variable and a response variable. The fitted line minimises the sum of squared differences between the true response and the predicted response. The coefficients (offset, gradient) of the fit can be tested for significance."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-      <img src="" class="logo" alt=""><h1>Univariate Classical Least Squares Regression</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/classical_lsq_class.R" class="external-link"><code>R/classical_lsq_class.R</code></a></small>
+
+      <h1>Univariate Classical Least Squares Regression</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/classical_lsq_class.R" class="external-link"><code>R/classical_lsq_class.R</code></a></small>
       <div class="d-none name"><code>classical_lsq.Rd</code></div>
     </div>
 
@@ -59,39 +47,39 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>alpha</dt>
+
+
+<dl><dt id="arg-alpha">alpha<a class="anchor" aria-label="anchor" href="#arg-alpha"></a></dt>
 <dd><p>(numeric) The p-value cutoff for determining significance. The default is <code>0.05</code>.<br></p></dd>
 
 
-<dt>mtc</dt>
+<dt id="arg-mtc">mtc<a class="anchor" aria-label="anchor" href="#arg-mtc"></a></dt>
 <dd><p>(character) Multiple test correction method. Allowed values are limited to the following:</p><ul><li><p><code>"bonferroni"</code>: Bonferroni correction in which the p-values are multiplied by the number of comparisons.</p></li>
 <li><p><code>"fdr"</code>: Benjamini and Hochberg False Discovery Rate correction.</p></li>
 <li><p><code>"none"</code>: No correction.</p></li>
 </ul><p>The default is <code>"fdr"</code>.</p></dd>
 
 
-<dt>factor_names</dt>
+<dt id="arg-factor-names">factor_names<a class="anchor" aria-label="anchor" href="#arg-factor-names"></a></dt>
 <dd><p>(character, list) The column names to regress against. If a character vector then the same list is used ofr all features. If a list of character vectors is provided it is assumed there is a different set of columns for each feature.</p></dd>
 
 
-<dt>intercept</dt>
+<dt id="arg-intercept">intercept<a class="anchor" aria-label="anchor" href="#arg-intercept"></a></dt>
 <dd><p>(logical) Model intercept. Allowed values are limited to the following:</p><ul><li><p><code>"TRUE"</code>: An intercept term is included in the model.</p></li>
 <li><p><code>"FALSE"</code>: An intercept term is not included in the model.</p></li>
 </ul><p>The default is <code>TRUE</code>.<br></p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>classical_lsq</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>coefficients</code></td><td>(data.frame) The regression coefficients for each term in the model.</td></tr><tr><td><code>p_value</code></td><td>(data.frame) The probability of observing the calculated statistic if the null hypothesis is true.</td></tr><tr><td><code>significant</code></td><td>(data.frame) True/False indicating whether the p-value computed for each variable is less than the threshold.</td></tr><tr><td><code>r_squared</code></td><td>(data.frame) The value of R Squared for the fitted model.</td></tr><tr><td><code>adj_r_squared</code></td><td>(data.frame) The value ofAdjusted  R Squared for the fitted model.</td></tr></table></div>
+    <p>A  <code>classical_lsq</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>coefficients</code></td><td>(data.frame) The regression coefficients for each term in the model.</td></tr><tr><td><code>p_value</code></td><td>(data.frame) The probability of observing the calculated statistic if the null hypothesis is true.</td></tr><tr><td><code>significant</code></td><td>(data.frame) True/False indicating whether the p-value computed for each variable is less than the threshold.</td></tr><tr><td><code>r_squared</code></td><td>(data.frame) The value of R Squared for the fitted model.</td></tr><tr><td><code>adj_r_squared</code></td><td>(data.frame) The value ofAdjusted  R Squared for the fitted model.</td></tr></table></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>classical_lsq</code> object inherits the following <code>struct</code> classes: <br><br><code>[classical_lsq]</code> &gt;&gt; <code>[model]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -109,23 +97,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="model_apply.html">model_apply</a></span><span class="op">(</span><span class="va">M</span>,<span class="va">D</span><span class="op">)</span></span></span>
 </code></pre></div>
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diff --git a/reference/compare_dist-1.png b/reference/compare_dist-1.png
index d5678d0..6c08fe0 100644
Binary files a/reference/compare_dist-1.png and b/reference/compare_dist-1.png differ
diff --git a/reference/compare_dist.html b/reference/compare_dist.html
index d3bce6a..f67d7c9 100644
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@@ -1,50 +1,38 @@
 <!DOCTYPE html>
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
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-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Compare distributions</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/dataset_chart_classes.R" class="external-link"><code>R/dataset_chart_classes.R</code></a></small>
+
+      <h1>Compare distributions</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/dataset_chart_classes.R" class="external-link"><code>R/dataset_chart_classes.R</code></a></small>
       <div class="d-none name"><code>compare_dist.Rd</code></div>
     </div>
 
@@ -59,26 +47,26 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>factor_name</dt>
+
+
+<dl><dt id="arg-factor-name">factor_name<a class="anchor" aria-label="anchor" href="#arg-factor-name"></a></dt>
 <dd><p>(character) The name of a sample-meta column to use.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>
+    <p>A  <code>
 compare_dist
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>compare_dist</code> object inherits the following <code>struct</code> classes: <br><br><code>[compare_dist]</code> &gt;&gt; <code>[chart]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -92,16 +80,24 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">D2</span><span class="op">=</span><span class="fu"><a href="MTBLS79_DatasetExperiment.html">MTBLS79_DatasetExperiment</a></span><span class="op">(</span>filtered<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">compare_dist</span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'Class'</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="chart_plot.html">chart_plot</a></span><span class="op">(</span><span class="va">C</span>,<span class="va">D1</span>,<span class="va">D2</span><span class="op">)</span></span></span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Removed 109 rows containing non-finite values (`stat_boxplot()`).</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Removed 109 rows containing non-finite values (`stat_boxplot()`).</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Removed 8012 rows containing non-finite values (`stat_bin()`).</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Removed 8012 rows containing non-finite values (`stat_bin()`).</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Removed 10623 rows containing non-finite values (`stat_bin()`).</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Removed 6 rows containing missing values (`geom_path()`).</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Removed 10623 rows containing non-finite values (`stat_bin()`).</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Removed 6 rows containing missing values (`geom_path()`).</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Removed 109 rows containing non-finite values (`stat_boxplot()`).</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Removed 109 rows containing non-finite values (`stat_boxplot()`).</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Removed 109 rows containing non-finite outside the scale range</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> (`stat_boxplot()`).</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Removed 109 rows containing non-finite outside the scale range</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> (`stat_boxplot()`).</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Removed 8012 rows containing non-finite outside the scale range (`stat_bin()`).</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Removed 8012 rows containing non-finite outside the scale range (`stat_bin()`).</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Removed 10623 rows containing non-finite outside the scale range</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> (`stat_bin()`).</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Removed 6 rows containing missing values or values outside the scale range</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> (`geom_path()`).</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Removed 10623 rows containing non-finite outside the scale range</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> (`stat_bin()`).</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Removed 6 rows containing missing values or values outside the scale range</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> (`geom_path()`).</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Removed 109 rows containing non-finite outside the scale range</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> (`stat_boxplot()`).</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Removed 109 rows containing non-finite outside the scale range</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> (`stat_boxplot()`).</span>
 <span class="r-plt img"><img src="compare_dist-1.png" alt="" width="700" height="433"></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> TableGrob (4 x 2) "arrange": 4 grobs</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   z     cells    name           grob</span>
@@ -111,23 +107,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> 4 4 (2-4,2-2) arrange gtable[layout]</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
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   </body></html>
 
diff --git a/reference/confounders_clsq.html b/reference/confounders_clsq.html
index c4f94f1..d585085 100644
--- a/reference/confounders_clsq.html
+++ b/reference/confounders_clsq.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Univariate least squares regression models are used to compare models with and without potential confounding factors included. The change in coefficients (delta) is then computed for each potential confounding factor. Factors with a large delta are said to be having a large impact on the model and are therefore confounding. p-values are computed for models with confounders included to reduce potential false positives. Only suitable for main factors with 2 levels."><title>Check for confounding factors — confounders_clsq • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Check for confounding factors — confounders_clsq"><meta property="og:description" content="Univariate least squares regression models are used to compare models with and without potential confounding factors included. The change in coefficients (delta) is then computed for each potential confounding factor. Factors with a large delta are said to be having a large impact on the model and are therefore confounding. p-values are computed for models with confounders included to reduce potential false positives. Only suitable for main factors with 2 levels."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
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-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Check for confounding factors — confounders_clsq • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Check for confounding factors — confounders_clsq"><meta name="description" content="Univariate least squares regression models are used to compare models with and without potential confounding factors included. The change in coefficients (delta) is then computed for each potential confounding factor. Factors with a large delta are said to be having a large impact on the model and are therefore confounding. p-values are computed for models with confounders included to reduce potential false positives. Only suitable for main factors with 2 levels."><meta property="og:description" content="Univariate least squares regression models are used to compare models with and without potential confounding factors included. The change in coefficients (delta) is then computed for each potential confounding factor. Factors with a large delta are said to be having a large impact on the model and are therefore confounding. p-values are computed for models with confounders included to reduce potential false positives. Only suitable for main factors with 2 levels."></head><body>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Check for confounding factors</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/confounders_clsq_class.R" class="external-link"><code>R/confounders_clsq_class.R</code></a></small>
+
+      <h1>Check for confounding factors</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/confounders_clsq_class.R" class="external-link"><code>R/confounders_clsq_class.R</code></a></small>
       <div class="d-none name"><code>confounders_clsq.Rd</code></div>
     </div>
 
@@ -66,41 +54,41 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>alpha</dt>
+
+
+<dl><dt id="arg-alpha">alpha<a class="anchor" aria-label="anchor" href="#arg-alpha"></a></dt>
 <dd><p>(numeric) The p-value cutoff for determining significance. The default is <code>0.05</code>.<br></p></dd>
 
 
-<dt>mtc</dt>
+<dt id="arg-mtc">mtc<a class="anchor" aria-label="anchor" href="#arg-mtc"></a></dt>
 <dd><p>(character) Multiple test correction method. Allowed values are limited to the following:</p><ul><li><p><code>"bonferroni"</code>: Bonferroni correction in which the p-values are multiplied by the number of comparisons.</p></li>
 <li><p><code>"fdr"</code>: Benjamini and Hochberg False Discovery Rate correction.</p></li>
 <li><p><code>"none"</code>: No correction.</p></li>
 </ul><p>The default is <code>"fdr"</code>.</p></dd>
 
 
-<dt>factor_name</dt>
+<dt id="arg-factor-name">factor_name<a class="anchor" aria-label="anchor" href="#arg-factor-name"></a></dt>
 <dd><p>(character) The name of the main factor with which other factors may be confounding.</p></dd>
 
 
-<dt>confounding_factors</dt>
+<dt id="arg-confounding-factors">confounding_factors<a class="anchor" aria-label="anchor" href="#arg-confounding-factors"></a></dt>
 <dd><p>(character) The name(s) of factor(s) that are potential confounding factors.</p></dd>
 
 
-<dt>threshold</dt>
+<dt id="arg-threshold">threshold<a class="anchor" aria-label="anchor" href="#arg-threshold"></a></dt>
 <dd><p>(numeric) Factors with a delta greater than the the threshold are considered to be confounding. The default is <code>0.15</code>.<br></p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>confounders_clsq</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>coefficients</code></td><td>(data.frame)</td></tr><tr><td><code>p_value</code></td><td>(data.frame)</td></tr><tr><td><code>significant</code></td><td>(data.frame)</td></tr><tr><td><code>percent_change</code></td><td>(data.frame)</td></tr><tr><td><code>potential_confounders</code></td><td>(list)</td></tr></table></div>
+    <p>A  <code>confounders_clsq</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>coefficients</code></td><td>(data.frame)</td></tr><tr><td><code>p_value</code></td><td>(data.frame)</td></tr><tr><td><code>significant</code></td><td>(data.frame)</td></tr><tr><td><code>percent_change</code></td><td>(data.frame)</td></tr><tr><td><code>potential_confounders</code></td><td>(list)</td></tr></table></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>confounders_clsq</code> object inherits the following <code>struct</code> classes: <br><br><code>[confounders_clsq]</code> &gt;&gt; <code>[model]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -124,23 +112,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="model_apply.html">model_apply</a></span><span class="op">(</span><span class="va">M</span>,<span class="va">D</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
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diff --git a/reference/confounders_lsq_barchart-1.png b/reference/confounders_lsq_barchart-1.png
index 2cfc060..69a3d9c 100644
Binary files a/reference/confounders_lsq_barchart-1.png and b/reference/confounders_lsq_barchart-1.png differ
diff --git a/reference/confounders_lsq_barchart.html b/reference/confounders_lsq_barchart.html
index 74e2bd0..d28904e 100644
--- a/reference/confounders_lsq_barchart.html
+++ b/reference/confounders_lsq_barchart.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="A barchart of the relative change (delta) in regression coefficient when potential confounding factors are included, and excluded, from the model. Factors with a large delta are considered to be confounding factors."><title>Confounding factor relative change barchart — confounders_lsq_barchart • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Confounding factor relative change barchart — confounders_lsq_barchart"><meta property="og:description" content="A barchart of the relative change (delta) in regression coefficient when potential confounding factors are included, and excluded, from the model. Factors with a large delta are considered to be confounding factors."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Confounding factor relative change barchart — confounders_lsq_barchart • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Confounding factor relative change barchart — confounders_lsq_barchart"><meta name="description" content="A barchart of the relative change (delta) in regression coefficient when potential confounding factors are included, and excluded, from the model. Factors with a large delta are considered to be confounding factors."><meta property="og:description" content="A barchart of the relative change (delta) in regression coefficient when potential confounding factors are included, and excluded, from the model. Factors with a large delta are considered to be confounding factors."></head><body>
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
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+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
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+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
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+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Confounding factor relative change barchart</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/confounders_clsq_class.R" class="external-link"><code>R/confounders_clsq_class.R</code></a></small>
+
+      <h1>Confounding factor relative change barchart</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/confounders_clsq_class.R" class="external-link"><code>R/confounders_clsq_class.R</code></a></small>
       <div class="d-none name"><code>confounders_lsq_barchart.Rd</code></div>
     </div>
 
@@ -59,30 +47,30 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>feature_to_plot</dt>
+
+
+<dl><dt id="arg-feature-to-plot">feature_to_plot<a class="anchor" aria-label="anchor" href="#arg-feature-to-plot"></a></dt>
 <dd><p>(numeric, character, integer) The column name of the feature to be plotted.</p></dd>
 
 
-<dt>threshold</dt>
+<dt id="arg-threshold">threshold<a class="anchor" aria-label="anchor" href="#arg-threshold"></a></dt>
 <dd><p>(numeric) A horizontal line is plotted to indicate the threshold. The default is <code>10</code>.<br></p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>
+    <p>A  <code>
 confounders_lsq_barchart
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>confounders_lsq_barchart</code> object inherits the following <code>struct</code> classes: <br><br><code>[confounders_lsq_barchart]</code> &gt;&gt; <code>[chart]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -106,23 +94,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-plt img"><img src="confounders_lsq_barchart-1.png" alt="" width="700" height="433"></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/confounders_lsq_boxplot-1.png b/reference/confounders_lsq_boxplot-1.png
index ac924a4..6b4d6fc 100644
Binary files a/reference/confounders_lsq_boxplot-1.png and b/reference/confounders_lsq_boxplot-1.png differ
diff --git a/reference/confounders_lsq_boxplot.html b/reference/confounders_lsq_boxplot.html
index d526eac..1819feb 100644
--- a/reference/confounders_lsq_boxplot.html
+++ b/reference/confounders_lsq_boxplot.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="A boxplot of the relative change (delta) in regression coefficient when potential confounding factors are included, and excluded, from the model. Factors with a large delta are considered to be confounding factors."><title>Confounding factor relative change boxplot — confounders_lsq_boxplot • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Confounding factor relative change boxplot — confounders_lsq_boxplot"><meta property="og:description" content="A boxplot of the relative change (delta) in regression coefficient when potential confounding factors are included, and excluded, from the model. Factors with a large delta are considered to be confounding factors."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.17.3</small>
+
 
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
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-    <a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Confounding factor relative change boxplot</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/confounders_clsq_class.R" class="external-link"><code>R/confounders_clsq_class.R</code></a></small>
+
+      <h1>Confounding factor relative change boxplot</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/confounders_clsq_class.R" class="external-link"><code>R/confounders_clsq_class.R</code></a></small>
       <div class="d-none name"><code>confounders_lsq_boxplot.Rd</code></div>
     </div>
 
@@ -59,26 +47,26 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>threshold</dt>
+
+
+<dl><dt id="arg-threshold">threshold<a class="anchor" aria-label="anchor" href="#arg-threshold"></a></dt>
 <dd><p>(numeric) A horizontal line is plotted to indicate the threshold. The default is <code>10</code>.<br></p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>
+    <p>A  <code>
 confounders_lsq_boxplot
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>confounders_lsq_boxplot</code> object inherits the following <code>struct</code> classes: <br><br><code>[confounders_lsq_boxplot]</code> &gt;&gt; <code>[chart]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -101,23 +89,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-plt img"><img src="confounders_lsq_boxplot-1.png" alt="" width="700" height="433"></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
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   </body></html>
 
diff --git a/reference/constant_sum_norm.html b/reference/constant_sum_norm.html
index 1cb5bef..f33a1ea 100644
--- a/reference/constant_sum_norm.html
+++ b/reference/constant_sum_norm.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Each sample is normalised such that the total signal is equal to one (or a scaling factor if specified)."><title>Normalisation to constant sum — constant_sum_norm • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Normalisation to constant sum — constant_sum_norm"><meta property="og:description" content="Each sample is normalised such that the total signal is equal to one (or a scaling factor if specified)."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Normalisation to constant sum — constant_sum_norm • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Normalisation to constant sum — constant_sum_norm"><meta name="description" content="Each sample is normalised such that the total signal is equal to one (or a scaling factor if specified)."><meta property="og:description" content="Each sample is normalised such that the total signal is equal to one (or a scaling factor if specified)."></head><body>
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     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
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+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Normalisation to constant sum</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/constant_sum_norm_class.R" class="external-link"><code>R/constant_sum_norm_class.R</code></a></small>
+
+      <h1>Normalisation to constant sum</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/constant_sum_norm_class.R" class="external-link"><code>R/constant_sum_norm_class.R</code></a></small>
       <div class="d-none name"><code>constant_sum_norm.Rd</code></div>
     </div>
 
@@ -59,22 +47,22 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>scaling_factor</dt>
+
+
+<dl><dt id="arg-scaling-factor">scaling_factor<a class="anchor" aria-label="anchor" href="#arg-scaling-factor"></a></dt>
 <dd><p>(numeric) The scaling factor applied after normalisation. The default is <code>1</code>.<br></p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>constant_sum_norm</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>normalised</code></td><td>(DatasetExperiment) A DatasetExperiment object containing the normalised data.</td></tr><tr><td><code>coeff</code></td><td>(data.frame) The sum of each row, used to normalise the samples.</td></tr></table></div>
+    <p>A  <code>constant_sum_norm</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>normalised</code></td><td>(DatasetExperiment) A DatasetExperiment object containing the normalised data.</td></tr><tr><td><code>coeff</code></td><td>(data.frame) The sum of each row, used to normalise the samples.</td></tr></table></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>constant_sum_norm</code> object inherits the following <code>struct</code> classes: <br><br><code>[constant_sum_norm]</code> &gt;&gt; <code>[model]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -88,23 +76,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
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-  
 
-  
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+
 
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diff --git a/reference/corr_coef.html b/reference/corr_coef.html
index 40d758c..10d71bf 100644
--- a/reference/corr_coef.html
+++ b/reference/corr_coef.html
@@ -1,50 +1,38 @@
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-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="The correlation between features and a set of continuous factor are calculated. Multiple-test corrected p-values are used to indicate whether the computed coefficients may have occurred by chance."><title>Correlation coefficient — corr_coef • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Correlation coefficient — corr_coef"><meta property="og:description" content="The correlation between features and a set of continuous factor are calculated. Multiple-test corrected p-values are used to indicate whether the computed coefficients may have occurred by chance."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-      <img src="" class="logo" alt=""><h1>Correlation coefficient</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/corr_coef_class.R" class="external-link"><code>R/corr_coef_class.R</code></a></small>
+
+      <h1>Correlation coefficient</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/corr_coef_class.R" class="external-link"><code>R/corr_coef_class.R</code></a></small>
       <div class="d-none name"><code>corr_coef.Rd</code></div>
     </div>
 
@@ -59,50 +47,50 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>alpha</dt>
+
+
+<dl><dt id="arg-alpha">alpha<a class="anchor" aria-label="anchor" href="#arg-alpha"></a></dt>
 <dd><p>(numeric) The p-value cutoff for determining significance. The default is <code>0.05</code>.<br></p></dd>
 
 
-<dt>mtc</dt>
+<dt id="arg-mtc">mtc<a class="anchor" aria-label="anchor" href="#arg-mtc"></a></dt>
 <dd><p>(character) Multiple test correction method. Allowed values are limited to the following:</p><ul><li><p><code>"bonferroni"</code>: Bonferroni correction in which the p-values are multiplied by the number of comparisons.</p></li>
 <li><p><code>"fdr"</code>: Benjamini and Hochberg False Discovery Rate correction.</p></li>
 <li><p><code>"none"</code>: No correction.</p></li>
 </ul><p>The default is <code>"fdr"</code>.</p></dd>
 
 
-<dt>factor_names</dt>
+<dt id="arg-factor-names">factor_names<a class="anchor" aria-label="anchor" href="#arg-factor-names"></a></dt>
 <dd><p>(character) The name of sample meta column(s) to use.</p></dd>
 
 
-<dt>method</dt>
+<dt id="arg-method">method<a class="anchor" aria-label="anchor" href="#arg-method"></a></dt>
 <dd><p>(character) Type of correlation. Allowed values are limited to the following:</p><ul><li><p><code>"kendall"</code>: Kendall's tau is computed.</p></li>
 <li><p><code>"pearson"</code>: Pearson product moment correlation is computed.</p></li>
 <li><p><code>"spearman"</code>: Spearman's rho statistic is computed.</p></li>
 </ul><p>The default is <code>"spearman"</code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>corr_coef</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>coeff</code></td><td>(data.frame) The value of the calculate statistics which is converted to a p-value when compared to a t-distribution.</td></tr><tr><td><code>p_value</code></td><td>(data.frame) The probability of observing the calculated statistic if the null hypothesis is true.</td></tr><tr><td><code>significant</code></td><td>(data.frame) True/False indicating whether the p-value computed for each variable is less than the threshold.</td></tr></table></div>
+    <p>A  <code>corr_coef</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>coeff</code></td><td>(data.frame) The value of the calculate statistics which is converted to a p-value when compared to a t-distribution.</td></tr><tr><td><code>p_value</code></td><td>(data.frame) The probability of observing the calculated statistic if the null hypothesis is true.</td></tr><tr><td><code>significant</code></td><td>(data.frame) True/False indicating whether the p-value computed for each variable is less than the threshold.</td></tr></table></div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>stats</code></p></li>
 </ul></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>corr_coef</code> object inherits the following <code>struct</code> classes: <br><br><code>[corr_coef]</code> &gt;&gt; <code>[model]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
     <div class="section level2">
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
-    <p>R Core Team (2023). <em>R: A Language and Environment for Statistical
+    <p>R Core Team (2024). <em>R: A Language and Environment for Statistical
 Computing</em>. R Foundation for Statistical Computing, Vienna, Austria.
 <a href="https://www.R-project.org/" class="external-link">https://www.R-project.org/</a>.</p>
     </div>
@@ -144,23 +132,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>NAs introduced by coercion</span>
 </code></pre></div>
     </div>
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diff --git a/reference/dfa_scores_plot-1.png b/reference/dfa_scores_plot-1.png
index 1f52534..f62d84a 100644
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diff --git a/reference/dfa_scores_plot.html b/reference/dfa_scores_plot.html
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--- a/reference/dfa_scores_plot.html
+++ b/reference/dfa_scores_plot.html
@@ -1,50 +1,38 @@
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+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>DFA scores plot</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/DFA_class.R" class="external-link"><code>R/DFA_class.R</code></a></small>
+
+      <h1>DFA scores plot</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/DFA_class.R" class="external-link"><code>R/DFA_class.R</code></a></small>
       <div class="d-none name"><code>dfa_scores_plot.Rd</code></div>
     </div>
 
@@ -68,22 +56,24 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>components</dt>
+
+
+<dl><dt id="arg-components">components<a class="anchor" aria-label="anchor" href="#arg-components"></a></dt>
 <dd><p>(numeric) The components selected for plotting. The default is <code>c(1, 2)</code>.</p></dd>
 
 
-<dt>points_to_label</dt>
+<dt id="arg-points-to-label">points_to_label<a class="anchor" aria-label="anchor" href="#arg-points-to-label"></a></dt>
 <dd><p>(character) Points to label. Allowed values are limited to the following:</p><ul><li><p><code>"none"</code>: No samples labels are displayed.</p></li>
 <li><p><code>"all"</code>: The labels for all samples are displayed.</p></li>
 <li><p><code>"outliers"</code>: Labels for for potential outlier samples are displayed.</p></li>
 </ul><p>The default is <code>"none"</code>.</p></dd>
 
 
-<dt>factor_name</dt>
+<dt id="arg-factor-name">factor_name<a class="anchor" aria-label="anchor" href="#arg-factor-name"></a></dt>
 <dd><p>(character) The name of a sample-meta column to use.</p></dd>
 
 
-<dt>ellipse</dt>
+<dt id="arg-ellipse">ellipse<a class="anchor" aria-label="anchor" href="#arg-ellipse"></a></dt>
 <dd><p>(character) Plot ellipses. Allowed values are limited to the following:</p><ul><li><p><code>"all"</code>: Hotelling T2 ellipses (p=0.95) are plotted for all groups and all samples.</p></li>
 <li><p><code>"group"</code>: Hotelling T2 ellipses (p=0.95) are plotted for all groups.</p></li>
 <li><p><code>"none"</code>: Ellipses are not included on the plot.</p></li>
@@ -91,27 +81,25 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </ul><p>The default is <code>"all"</code>.</p></dd>
 
 
-<dt>label_filter</dt>
+<dt id="arg-label-filter">label_filter<a class="anchor" aria-label="anchor" href="#arg-label-filter"></a></dt>
 <dd><p>(character) Labels are only plotted for the named groups. If zero-length then all groups are included. The default is <code>character(0)</code>.</p></dd>
 
 
-<dt>label_factor</dt>
+<dt id="arg-label-factor">label_factor<a class="anchor" aria-label="anchor" href="#arg-label-factor"></a></dt>
 <dd><p>(character) The column name of sample_meta to use for labelling samples on the plot. "rownames" will use the row names from sample_meta. The default is <code>"rownames"</code>.</p></dd>
 
 
-<dt>label_size</dt>
+<dt id="arg-label-size">label_size<a class="anchor" aria-label="anchor" href="#arg-label-size"></a></dt>
 <dd><p>(numeric) The text size of labels. Note this is not in Font Units. The default is <code>3.88</code>.<br></p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>
+    <p>A  <code>
 dfa_scores_plot
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
@@ -123,7 +111,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 </ul></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>dfa_scores_plot</code> object inherits the following <code>struct</code> classes: <br><br><code>[dfa_scores_plot]</code> &gt;&gt; <code>[chart]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -153,22 +141,22 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="model_apply.html">model_apply</a></span><span class="op">(</span><span class="va">M</span>,<span class="va">D</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">dfa_scores_plot</span><span class="op">(</span>factor_name <span class="op">=</span> <span class="st">'Species'</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="chart_plot.html">chart_plot</a></span><span class="op">(</span><span class="va">C</span>,<span class="va">M</span><span class="op">[</span><span class="fl">2</span><span class="op">]</span><span class="op">)</span></span></span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>The following aesthetics were dropped during statistical transformation: <span style="color: #00BB00;">label</span></span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>The following aesthetics were dropped during statistical transformation: <span style="color: #00BB00;">label</span>.</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> This can happen when ggplot fails to infer the correct grouping structure in</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span>   the data.</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Did you forget to specify a `group` aesthetic or to convert a numerical</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span>   variable into a factor?</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>The following aesthetics were dropped during statistical transformation: <span style="color: #00BB00;">label</span></span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>The following aesthetics were dropped during statistical transformation: <span style="color: #00BB00;">label</span>.</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> This can happen when ggplot fails to infer the correct grouping structure in</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span>   the data.</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Did you forget to specify a `group` aesthetic or to convert a numerical</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span>   variable into a factor?</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>The following aesthetics were dropped during statistical transformation: <span style="color: #00BB00;">label</span></span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>The following aesthetics were dropped during statistical transformation: <span style="color: #00BB00;">label</span>.</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> This can happen when ggplot fails to infer the correct grouping structure in</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span>   the data.</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Did you forget to specify a `group` aesthetic or to convert a numerical</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span>   variable into a factor?</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>The following aesthetics were dropped during statistical transformation: <span style="color: #00BB00;">label</span></span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>The following aesthetics were dropped during statistical transformation: <span style="color: #00BB00;">label</span>.</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> This can happen when ggplot fails to infer the correct grouping structure in</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span>   the data.</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Did you forget to specify a `group` aesthetic or to convert a numerical</span>
@@ -177,23 +165,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
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   </body></html>
 
diff --git a/reference/dratio_filter.html b/reference/dratio_filter.html
index c25c726..0b2b85c 100644
--- a/reference/dratio_filter.html
+++ b/reference/dratio_filter.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="The dispersion ratio (d-ratio) compares the standard deviation (or non-parametric equivalent) of the Quality Control (QC) samples relative to the standard deviation (or non-parametric equivalent) of the samples for each feature. If the d-ratio is greater than a predefined threshold then the observed sample variance could be due to technical variance and the feature is removed."><title>Dispersion ratio filter — dratio_filter • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Dispersion ratio filter — dratio_filter"><meta property="og:description" content="The dispersion ratio (d-ratio) compares the standard deviation (or non-parametric equivalent) of the Quality Control (QC) samples relative to the standard deviation (or non-parametric equivalent) of the samples for each feature. If the d-ratio is greater than a predefined threshold then the observed sample variance could be due to technical variance and the feature is removed."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Dispersion ratio filter — dratio_filter • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Dispersion ratio filter — dratio_filter"><meta name="description" content="The dispersion ratio (d-ratio) compares the standard deviation (or non-parametric equivalent) of the Quality Control (QC) samples relative to the standard deviation (or non-parametric equivalent) of the samples for each feature. If the d-ratio is greater than a predefined threshold then the observed sample variance could be due to technical variance and the feature is removed."><meta property="og:description" content="The dispersion ratio (d-ratio) compares the standard deviation (or non-parametric equivalent) of the Quality Control (QC) samples relative to the standard deviation (or non-parametric equivalent) of the samples for each feature. If the d-ratio is greater than a predefined threshold then the observed sample variance could be due to technical variance and the feature is removed."></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary"><div class="container">
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+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.17.3</small>
+
 
-    
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-      <ul class="navbar-nav me-auto"><li class="active nav-item">
-  <a class="nav-link" href="../reference/index.html">Reference</a>
-</li>
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Dispersion ratio filter</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/d_ratio_filter_class.R" class="external-link"><code>R/d_ratio_filter_class.R</code></a></small>
+
+      <h1>Dispersion ratio filter</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/d_ratio_filter_class.R" class="external-link"><code>R/d_ratio_filter_class.R</code></a></small>
       <div class="d-none name"><code>dratio_filter.Rd</code></div>
     </div>
 
@@ -66,42 +54,42 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>threshold</dt>
+
+
+<dl><dt id="arg-threshold">threshold<a class="anchor" aria-label="anchor" href="#arg-threshold"></a></dt>
 <dd><p>(numeric) The threshold above which features are removed. The default is <code>20</code>.<br></p></dd>
 
 
-<dt>qc_label</dt>
+<dt id="arg-qc-label">qc_label<a class="anchor" aria-label="anchor" href="#arg-qc-label"></a></dt>
 <dd><p>(character) The label used to identify QC samples. The default is <code>"QC"</code>.</p></dd>
 
 
-<dt>factor_name</dt>
+<dt id="arg-factor-name">factor_name<a class="anchor" aria-label="anchor" href="#arg-factor-name"></a></dt>
 <dd><p>(character) The name of a sample-meta column to use.</p></dd>
 
 
-<dt>method</dt>
+<dt id="arg-method">method<a class="anchor" aria-label="anchor" href="#arg-method"></a></dt>
 <dd><p>(character) dratio method. Allowed values are limited to the following:</p><ul><li><p><code>"ratio"</code>: Dispersion of the QCs divided by the dispersion of the samples. Corresponds to Eq 4 in  Broadhurst et al (2018).</p></li>
 <li><p><code>"euclidean"</code>: Dispersion of the QCs divided by the euclidean length of the total dispersion. Total dispersion is estimated from the QC and Sample dispersion by assuming that they are orthogonal. Corresponds to Eq 5 in Broadhurst et al (2018).</p></li>
 </ul><p>The default is <code>"ratio"</code>.</p></dd>
 
 
-<dt>dispersion</dt>
+<dt id="arg-dispersion">dispersion<a class="anchor" aria-label="anchor" href="#arg-dispersion"></a></dt>
 <dd><p>(character) Dispersion method. Allowed values are limited to the following:</p><ul><li><p><code>"sd"</code>: Dispersion is estimated using the standard deviation.</p></li>
 <li><p><code>"mad"</code>: Dispersion is estimated using the median absolute deviation.</p></li>
 </ul><p>The default is <code>"sd"</code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>dratio_filter</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>filtered</code></td><td>(DatasetExperiment) A DatasetExperiment object containing the filtered data.</td></tr><tr><td><code>flags</code></td><td>(data.frame) Flag indicating whether the feature was rejected by the filter or not.</td></tr><tr><td><code>d_ratio</code></td><td>(data.frame)</td></tr></table></div>
+    <p>A  <code>dratio_filter</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>filtered</code></td><td>(DatasetExperiment) A DatasetExperiment object containing the filtered data.</td></tr><tr><td><code>flags</code></td><td>(data.frame) Flag indicating whether the feature was rejected by the filter or not.</td></tr><tr><td><code>d_ratio</code></td><td>(data.frame)</td></tr></table></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>dratio_filter</code> object inherits the following <code>struct</code> classes: <br><br><code>[dratio_filter]</code> &gt;&gt; <code>[model]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -128,23 +116,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="model_apply.html">model_apply</a></span><span class="op">(</span><span class="va">M</span>,<span class="va">D</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
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-  
 
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diff --git a/reference/equal_split.html b/reference/equal_split.html
index 8a05d4c..aeaf4bd 100644
--- a/reference/equal_split.html
+++ b/reference/equal_split.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Samples are randomly chosen from each level such that the training set has equal numbers of samples for all levels. The number of samples is based on the input proportion and the smallest group size."><title>Equal group sized sampling — equal_split • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Equal group sized sampling — equal_split"><meta property="og:description" content="Samples are randomly chosen from each level such that the training set has equal numbers of samples for all levels. The number of samples is based on the input proportion and the smallest group size."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Equal group sized sampling — equal_split • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Equal group sized sampling — equal_split"><meta name="description" content="Samples are randomly chosen from each level such that the training set has equal numbers of samples for all levels. The number of samples is based on the input proportion and the smallest group size."><meta property="og:description" content="Samples are randomly chosen from each level such that the training set has equal numbers of samples for all levels. The number of samples is based on the input proportion and the smallest group size."></head><body>
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+
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
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+
 
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Equal group sized sampling</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/equal_split_class.R" class="external-link"><code>R/equal_split_class.R</code></a></small>
+
+      <h1>Equal group sized sampling</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/equal_split_class.R" class="external-link"><code>R/equal_split_class.R</code></a></small>
       <div class="d-none name"><code>equal_split.Rd</code></div>
     </div>
 
@@ -59,26 +47,26 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>p_train</dt>
+
+
+<dl><dt id="arg-p-train">p_train<a class="anchor" aria-label="anchor" href="#arg-p-train"></a></dt>
 <dd><p>(numeric) The proportion of samples selected for the training set. The default is <code>1</code>.<br></p></dd>
 
 
-<dt>factor_name</dt>
+<dt id="arg-factor-name">factor_name<a class="anchor" aria-label="anchor" href="#arg-factor-name"></a></dt>
 <dd><p>(character) The name of a sample-meta column to use.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>equal_split</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>training</code></td><td>(DatasetExperiment) A DatasetExperiment object containing samples selected for the training set.</td></tr><tr><td><code>testing</code></td><td>(DatasetExperiment) A DatasetExperiment object containing samples selected for the testing set.</td></tr></table></div>
+    <p>A  <code>equal_split</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>training</code></td><td>(DatasetExperiment) A DatasetExperiment object containing samples selected for the training set.</td></tr><tr><td><code>testing</code></td><td>(DatasetExperiment) A DatasetExperiment object containing samples selected for the testing set.</td></tr></table></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>equal_split</code> object inherits the following <code>struct</code> classes: <br><br><code>[equal_split]</code> &gt;&gt; <code>[split_data]</code> &gt;&gt; <code>[model]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -94,23 +82,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="model_apply.html">model_apply</a></span><span class="op">(</span><span class="va">M</span>,<span class="va">D</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
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diff --git a/reference/feature_boxplot.html b/reference/feature_boxplot.html
index 321f7c7..81a723f 100644
--- a/reference/feature_boxplot.html
+++ b/reference/feature_boxplot.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="A boxplot to visualise the distribution of values within a feature."><title>Feature boxplot — feature_boxplot • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Feature boxplot — feature_boxplot"><meta property="og:description" content="A boxplot to visualise the distribution of values within a feature."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Feature boxplot — feature_boxplot • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Feature boxplot — feature_boxplot"><meta name="description" content="A boxplot to visualise the distribution of values within a feature."><meta property="og:description" content="A boxplot to visualise the distribution of values within a feature."></head><body>
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-      <ul class="navbar-nav me-auto"><li class="active nav-item">
-  <a class="nav-link" href="../reference/index.html">Reference</a>
-</li>
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
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-    <a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a>
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-      </ul><form class="form-inline my-2 my-lg-0" role="search">
-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Feature boxplot</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/dataset_chart_classes.R" class="external-link"><code>R/dataset_chart_classes.R</code></a></small>
+
+      <h1>Feature boxplot</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/dataset_chart_classes.R" class="external-link"><code>R/dataset_chart_classes.R</code></a></small>
       <div class="d-none name"><code>feature_boxplot.Rd</code></div>
     </div>
 
@@ -68,56 +56,56 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>label_outliers</dt>
+
+
+<dl><dt id="arg-label-outliers">label_outliers<a class="anchor" aria-label="anchor" href="#arg-label-outliers"></a></dt>
 <dd><p>(logical) Label outliers. Allowed values are limited to the following:</p><ul><li><p><code>"TRUE"</code>: The index for outlier samples is included on the plot.</p></li>
 <li><p><code>"FALSE"</code>: No labels are displayed.</p></li>
 </ul><p>The default is <code>TRUE</code>.<br></p></dd>
 
 
-<dt>feature_to_plot</dt>
+<dt id="arg-feature-to-plot">feature_to_plot<a class="anchor" aria-label="anchor" href="#arg-feature-to-plot"></a></dt>
 <dd><p>(character, numeric, integer) The column name of the plotted feature.</p></dd>
 
 
-<dt>factor_name</dt>
+<dt id="arg-factor-name">factor_name<a class="anchor" aria-label="anchor" href="#arg-factor-name"></a></dt>
 <dd><p>(character) The name of a sample-meta column to use.</p></dd>
 
 
-<dt>show_counts</dt>
+<dt id="arg-show-counts">show_counts<a class="anchor" aria-label="anchor" href="#arg-show-counts"></a></dt>
 <dd><p>(logical) Show counts. Allowed values are limited to the following:</p><ul><li><p><code>"TRUE"</code>: The number of samples for each box is displayed.</p></li>
 <li><p><code>"FALSE"</code>: The number of samples for each box is not displayed.</p></li>
 </ul><p>The default is <code>TRUE</code>.<br></p></dd>
 
 
-<dt>style</dt>
+<dt id="arg-style">style<a class="anchor" aria-label="anchor" href="#arg-style"></a></dt>
 <dd><p>(character) Plot style. Allowed values are limited to the following:</p><ul><li><p><code>"boxplot"</code>: Boxplot style.</p></li>
 <li><p><code>"violin"</code>: Violon plot style.</p></li>
 </ul><p>The default is <code>"boxplot"</code>.</p></dd>
 
 
-<dt>jitter</dt>
+<dt id="arg-jitter">jitter<a class="anchor" aria-label="anchor" href="#arg-jitter"></a></dt>
 <dd><p>(logical) Include points plotted with added jitter. The default is <code>FALSE</code>.<br></p></dd>
 
 
-<dt>fill</dt>
+<dt id="arg-fill">fill<a class="anchor" aria-label="anchor" href="#arg-fill"></a></dt>
 <dd><p>(logical) Block fill the boxes or violins with the group colour. The default is <code>FALSE</code>.<br></p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>
+    <p>A  <code>
 feature_boxplot
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>feature_boxplot</code> object inherits the following <code>struct</code> classes: <br><br><code>[feature_boxplot]</code> &gt;&gt; <code>[chart]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -144,23 +132,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> input params:  label_outliers, feature_to_plot, factor_name, show_counts, style, jitter, fill </span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
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+
 
   </body></html>
 
diff --git a/reference/feature_profile-1.png b/reference/feature_profile-1.png
index 5819df5..bcd5945 100644
Binary files a/reference/feature_profile-1.png and b/reference/feature_profile-1.png differ
diff --git a/reference/feature_profile.html b/reference/feature_profile.html
index f37a7f8..352fa80 100644
--- a/reference/feature_profile.html
+++ b/reference/feature_profile.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="A plot visualising the change in intensity of a feature with a continuous variable such as time, dose, or run order."><title>Feature profile — feature_profile • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Feature profile — feature_profile"><meta property="og:description" content="A plot visualising the change in intensity of a feature with a continuous variable such as time, dose, or run order."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Feature profile — feature_profile • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Feature profile — feature_profile"><meta name="description" content="A plot visualising the change in intensity of a feature with a continuous variable such as time, dose, or run order."><meta property="og:description" content="A plot visualising the change in intensity of a feature with a continuous variable such as time, dose, or run order."></head><body>
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+
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.17.3</small>
+
 
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-  <a class="nav-link" href="../reference/index.html">Reference</a>
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Feature profile</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/feature_profile_class.R" class="external-link"><code>R/feature_profile_class.R</code></a></small>
+
+      <h1>Feature profile</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/feature_profile_class.R" class="external-link"><code>R/feature_profile_class.R</code></a></small>
       <div class="d-none name"><code>feature_profile.Rd</code></div>
     </div>
 
@@ -67,48 +55,48 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>run_order</dt>
+
+
+<dl><dt id="arg-run-order">run_order<a class="anchor" aria-label="anchor" href="#arg-run-order"></a></dt>
 <dd><p>(character) The sample-meta column name containing run order.</p></dd>
 
 
-<dt>qc_label</dt>
+<dt id="arg-qc-label">qc_label<a class="anchor" aria-label="anchor" href="#arg-qc-label"></a></dt>
 <dd><p>(character) The label used to identify QC samples.</p></dd>
 
 
-<dt>qc_column</dt>
+<dt id="arg-qc-column">qc_column<a class="anchor" aria-label="anchor" href="#arg-qc-column"></a></dt>
 <dd><p>(character) The sample-meta column name containing the labels used to identify QC samples.</p></dd>
 
 
-<dt>colour_by</dt>
+<dt id="arg-colour-by">colour_by<a class="anchor" aria-label="anchor" href="#arg-colour-by"></a></dt>
 <dd><p>(character) The sample-meta column name to used to colour the plot.</p></dd>
 
 
-<dt>feature_to_plot</dt>
+<dt id="arg-feature-to-plot">feature_to_plot<a class="anchor" aria-label="anchor" href="#arg-feature-to-plot"></a></dt>
 <dd><p>(numeric, character, integer) The name or column id of the plotted feature.</p></dd>
 
 
-<dt>plot_sd</dt>
+<dt id="arg-plot-sd">plot_sd<a class="anchor" aria-label="anchor" href="#arg-plot-sd"></a></dt>
 <dd><p>(logical) Plot standard deviation. Allowed values are limited to the following:</p><ul><li><p><code>"TRUE"</code>: Standard deviation of samples and QCs are included on the plot.</p></li>
 <li><p><code>"FALSE"</code>: Standard deviation is not plotted.</p></li>
 </ul><p>The default is <code>FALSE</code>.<br></p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>
+    <p>A  <code>
 feature_profile
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>feature_profile</code> object inherits the following <code>struct</code> classes: <br><br><code>[feature_profile]</code> &gt;&gt; <code>[chart]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -130,27 +118,28 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span>    colour_by<span class="op">=</span><span class="st">'Class'</span>,</span></span>
 <span class="r-in"><span>    feature_to_plot<span class="op">=</span><span class="fl">1</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="chart_plot.html">chart_plot</a></span><span class="op">(</span><span class="va">C</span>,<span class="va">D</span><span class="op">)</span></span></span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Removed 18 rows containing missing values (`geom_point()`).</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Removed 18 rows containing missing values or values outside the scale range</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> (`geom_point()`).</span>
 <span class="r-plt img"><img src="feature_profile-1.png" alt="" width="700" height="433"></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
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     </footer></div>
 
-  
 
-  
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diff --git a/reference/feature_profile_array.html b/reference/feature_profile_array.html
index 212485d..8976ee7 100644
--- a/reference/feature_profile_array.html
+++ b/reference/feature_profile_array.html
@@ -1,50 +1,38 @@
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-      <img src="" class="logo" alt=""><h1>Feature profile</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/feature_plot_array_class.R" class="external-link"><code>R/feature_plot_array_class.R</code></a></small>
+
+      <h1>Feature profile</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/feature_plot_array_class.R" class="external-link"><code>R/feature_plot_array_class.R</code></a></small>
       <div class="d-none name"><code>feature_profile_array.Rd</code></div>
     </div>
 
@@ -68,52 +56,52 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>run_order</dt>
+
+
+<dl><dt id="arg-run-order">run_order<a class="anchor" aria-label="anchor" href="#arg-run-order"></a></dt>
 <dd><p>(character) The sample-meta column name containing run order.</p></dd>
 
 
-<dt>qc_label</dt>
+<dt id="arg-qc-label">qc_label<a class="anchor" aria-label="anchor" href="#arg-qc-label"></a></dt>
 <dd><p>(character) The label used to identify QC samples.</p></dd>
 
 
-<dt>qc_column</dt>
+<dt id="arg-qc-column">qc_column<a class="anchor" aria-label="anchor" href="#arg-qc-column"></a></dt>
 <dd><p>(character) The sample-meta column name containing the labels used to identify QC samples.</p></dd>
 
 
-<dt>colour_by</dt>
+<dt id="arg-colour-by">colour_by<a class="anchor" aria-label="anchor" href="#arg-colour-by"></a></dt>
 <dd><p>(character) The sample-meta column name to used to colour the plot.</p></dd>
 
 
-<dt>feature_to_plot</dt>
+<dt id="arg-feature-to-plot">feature_to_plot<a class="anchor" aria-label="anchor" href="#arg-feature-to-plot"></a></dt>
 <dd><p>(numeric, character, integer) The name or column id of the plotted feature.</p></dd>
 
 
-<dt>nrow</dt>
+<dt id="arg-nrow">nrow<a class="anchor" aria-label="anchor" href="#arg-nrow"></a></dt>
 <dd><p>(numeric, integer) The number of rows in the plot. The default is <code>5</code>.<br></p></dd>
 
 
-<dt>log</dt>
+<dt id="arg-log">log<a class="anchor" aria-label="anchor" href="#arg-log"></a></dt>
 <dd><p>(logical) Log transform. Allowed values are limited to the following:</p><ul><li><p><code>"TRUE"</code>: The data is log tranformed before plotting.</p></li>
 <li><p><code>"FALSE"</code>: The data is not transformed before plotting.</p></li>
 </ul><p>The default is <code>TRUE</code>.<br></p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>
+    <p>A  <code>
 feature_profile_array
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>feature_profile_array</code> object inherits the following <code>struct</code> classes: <br><br><code>[feature_profile_array]</code> &gt;&gt; <code>[chart]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -139,27 +127,28 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span>    nrow<span class="op">=</span><span class="fl">1</span>,</span></span>
 <span class="r-in"><span>    log<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="chart_plot.html">chart_plot</a></span><span class="op">(</span><span class="va">C</span>,<span class="va">D</span><span class="op">)</span></span></span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Removed 23 rows containing missing values (`geom_point()`).</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Removed 23 rows containing missing values or values outside the scale range</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> (`geom_point()`).</span>
 <span class="r-plt img"><img src="feature_profile_array-1.png" alt="" width="700" height="433"></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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diff --git a/reference/filter_by_name.html b/reference/filter_by_name.html
index e05e68a..168cfd3 100644
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@@ -1,50 +1,38 @@
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+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Filter by name</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/filter_by_name_class.R" class="external-link"><code>R/filter_by_name_class.R</code></a></small>
+
+      <h1>Filter by name</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/filter_by_name_class.R" class="external-link"><code>R/filter_by_name_class.R</code></a></small>
       <div class="d-none name"><code>filter_by_name.Rd</code></div>
     </div>
 
@@ -59,30 +47,30 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>mode</dt>
+
+
+<dl><dt id="arg-mode">mode<a class="anchor" aria-label="anchor" href="#arg-mode"></a></dt>
 <dd><p>(character) The filtering mode controls whether samples/features are mode="included" or mode="excluded" based on their name. The default is <code>"exclude"</code>.</p></dd>
 
 
-<dt>dimension</dt>
+<dt id="arg-dimension">dimension<a class="anchor" aria-label="anchor" href="#arg-dimension"></a></dt>
 <dd><p>(character) The filtering dimensions controls whether dimension="sample" or dimension="variable" are filtered based on their name. The default is <code>"sample"</code>.</p></dd>
 
 
-<dt>names</dt>
+<dt id="arg-names">names<a class="anchor" aria-label="anchor" href="#arg-names"></a></dt>
 <dd><p>(character, numeric, logical) The name of features/samples to be filtered. Must be an exact match. Can also provide indexes (numeric) or logical.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>filter_by_name</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>filtered</code></td><td>(DatasetExperiment)</td></tr></table></div>
+    <p>A  <code>filter_by_name</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>filtered</code></td><td>(DatasetExperiment)</td></tr></table></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>filter_by_name</code> object inherits the following <code>struct</code> classes: <br><br><code>[filter_by_name]</code> &gt;&gt; <code>[model]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -99,23 +87,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="model_apply.html">model_apply</a></span><span class="op">(</span><span class="va">M</span>,<span class="va">D</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/filter_na_count.html b/reference/filter_na_count.html
index e0b5cc5..8cc69c2 100644
--- a/reference/filter_na_count.html
+++ b/reference/filter_na_count.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="The number of measured values is counted for each feature, and any feature with less than a predefined minimum number of values in each group is removed. If there are several factors, then the threshold is applied so that the minimum number of samples is present for all combinations (interactions) of groups."><title>Minimum number of measured values filter — filter_na_count • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Minimum number of measured values filter — filter_na_count"><meta property="og:description" content="The number of measured values is counted for each feature, and any feature with less than a predefined minimum number of values in each group is removed. If there are several factors, then the threshold is applied so that the minimum number of samples is present for all combinations (interactions) of groups."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Minimum number of measured values filter — filter_na_count • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Minimum number of measured values filter — filter_na_count"><meta name="description" content="The number of measured values is counted for each feature, and any feature with less than a predefined minimum number of values in each group is removed. If there are several factors, then the threshold is applied so that the minimum number of samples is present for all combinations (interactions) of groups."><meta property="og:description" content="The number of measured values is counted for each feature, and any feature with less than a predefined minimum number of values in each group is removed. If there are several factors, then the threshold is applied so that the minimum number of samples is present for all combinations (interactions) of groups."></head><body>
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     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.17.3</small>
+
 
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Minimum number of measured values filter</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/filter_na_count.R" class="external-link"><code>R/filter_na_count.R</code></a></small>
+
+      <h1>Minimum number of measured values filter</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/filter_na_count.R" class="external-link"><code>R/filter_na_count.R</code></a></small>
       <div class="d-none name"><code>filter_na_count.Rd</code></div>
     </div>
 
@@ -59,26 +47,26 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>threshold</dt>
+
+
+<dl><dt id="arg-threshold">threshold<a class="anchor" aria-label="anchor" href="#arg-threshold"></a></dt>
 <dd><p>(numeric) The minimum number of samples in each group/interaction.</p></dd>
 
 
-<dt>factor_name</dt>
+<dt id="arg-factor-name">factor_name<a class="anchor" aria-label="anchor" href="#arg-factor-name"></a></dt>
 <dd><p>(character) The name of a sample-meta column to use.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>filter_na_count</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>filtered</code></td><td>(DatasetExperiment) A DatasetExperiment object containing the filtered data.</td></tr><tr><td><code>count</code></td><td>(data.frame) The number of measured values in each group/interaction.</td></tr><tr><td><code>na_count</code></td><td>(data.frame) The number of missing values in each group/interaction.</td></tr><tr><td><code>flags</code></td><td>(data.frame) Flags to indicate which features were removed.</td></tr></table></div>
+    <p>A  <code>filter_na_count</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>filtered</code></td><td>(DatasetExperiment) A DatasetExperiment object containing the filtered data.</td></tr><tr><td><code>count</code></td><td>(data.frame) The number of measured values in each group/interaction.</td></tr><tr><td><code>na_count</code></td><td>(data.frame) The number of missing values in each group/interaction.</td></tr><tr><td><code>flags</code></td><td>(data.frame) Flags to indicate which features were removed.</td></tr></table></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>filter_na_count</code> object inherits the following <code>struct</code> classes: <br><br><code>[filter_na_count]</code> &gt;&gt; <code>[model]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -94,23 +82,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="model_apply.html">model_apply</a></span><span class="op">(</span><span class="va">M</span>,<span class="va">D</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/filter_smeta.html b/reference/filter_smeta.html
index e4e4657..1338966 100644
--- a/reference/filter_smeta.html
+++ b/reference/filter_smeta.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="The data is filtered by so that the named levels of a factor are included/excluded from the dataset."><title>Filter by sample meta data — filter_smeta • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Filter by sample meta data — filter_smeta"><meta property="og:description" content="The data is filtered by so that the named levels of a factor are included/excluded from the dataset."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Filter by sample meta data — filter_smeta • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Filter by sample meta data — filter_smeta"><meta name="description" content="The data is filtered by so that the named levels of a factor are included/excluded from the dataset."><meta property="og:description" content="The data is filtered by so that the named levels of a factor are included/excluded from the dataset."></head><body>
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     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.17.3</small>
+
 
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Filter by sample meta data</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/filter_smeta_class.R" class="external-link"><code>R/filter_smeta_class.R</code></a></small>
+
+      <h1>Filter by sample meta data</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/filter_smeta_class.R" class="external-link"><code>R/filter_smeta_class.R</code></a></small>
       <div class="d-none name"><code>filter_smeta.Rd</code></div>
     </div>
 
@@ -59,32 +47,32 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>mode</dt>
+
+
+<dl><dt id="arg-mode">mode<a class="anchor" aria-label="anchor" href="#arg-mode"></a></dt>
 <dd><p>(character) Mode of action. Allowed values are limited to the following:</p><ul><li><p><code>"include"</code>: Samples in the specified levels are retained.</p></li>
 <li><p><code>"exclude"</code>: Samples in the specified levels are excluded.</p></li>
 </ul><p>The default is <code>"include"</code>.</p></dd>
 
 
-<dt>levels</dt>
+<dt id="arg-levels">levels<a class="anchor" aria-label="anchor" href="#arg-levels"></a></dt>
 <dd><p>(character) The level name(s) for filtering.</p></dd>
 
 
-<dt>factor_name</dt>
+<dt id="arg-factor-name">factor_name<a class="anchor" aria-label="anchor" href="#arg-factor-name"></a></dt>
 <dd><p>(character) The name of a sample-meta column to use.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>filter_smeta</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>filtered</code></td><td>(DatasetExperiment)</td></tr></table></div>
+    <p>A  <code>filter_smeta</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>filtered</code></td><td>(DatasetExperiment)</td></tr></table></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>filter_smeta</code> object inherits the following <code>struct</code> classes: <br><br><code>[filter_smeta]</code> &gt;&gt; <code>[model]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -101,23 +89,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="model_apply.html">model_apply</a></span><span class="op">(</span><span class="va">M</span>,<span class="va">D</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
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diff --git a/reference/fisher_exact.html b/reference/fisher_exact.html
index 66e248b..f4944fe 100644
--- a/reference/fisher_exact.html
+++ b/reference/fisher_exact.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="A fisher exact test is used to analyse contingency tables by comparing the number of correctly/incorrectly predicted group labels. A multiple test corrected p-value indicates whether the number of measured values is significantly different between groups."><title>Fisher Exact Test — fisher_exact • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fisher Exact Test — fisher_exact"><meta property="og:description" content="A fisher exact test is used to analyse contingency tables by comparing the number of correctly/incorrectly predicted group labels. A multiple test corrected p-value indicates whether the number of measured values is significantly different between groups."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Fisher Exact Test — fisher_exact • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fisher Exact Test — fisher_exact"><meta name="description" content="A fisher exact test is used to analyse contingency tables by comparing the number of correctly/incorrectly predicted group labels. A multiple test corrected p-value indicates whether the number of measured values is significantly different between groups."><meta property="og:description" content="A fisher exact test is used to analyse contingency tables by comparing the number of correctly/incorrectly predicted group labels. A multiple test corrected p-value indicates whether the number of measured values is significantly different between groups."></head><body>
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.17.3</small>
+
 
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Fisher Exact Test</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/fisher_exact_class.R" class="external-link"><code>R/fisher_exact_class.R</code></a></small>
+
+      <h1>Fisher Exact Test</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/fisher_exact_class.R" class="external-link"><code>R/fisher_exact_class.R</code></a></small>
       <div class="d-none name"><code>fisher_exact.Rd</code></div>
     </div>
 
@@ -59,37 +47,37 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>alpha</dt>
+
+
+<dl><dt id="arg-alpha">alpha<a class="anchor" aria-label="anchor" href="#arg-alpha"></a></dt>
 <dd><p>(numeric) The p-value cutoff for determining significance. The default is <code>0.05</code>.<br></p></dd>
 
 
-<dt>mtc</dt>
+<dt id="arg-mtc">mtc<a class="anchor" aria-label="anchor" href="#arg-mtc"></a></dt>
 <dd><p>(character) Multiple test correction method. Allowed values are limited to the following:</p><ul><li><p><code>"bonferroni"</code>: Bonferroni correction in which the p-values are multiplied by the number of comparisons.</p></li>
 <li><p><code>"fdr"</code>: Benjamini and Hochberg False Discovery Rate correction.</p></li>
 <li><p><code>"none"</code>: No correction.</p></li>
 </ul><p>The default is <code>"fdr"</code>.</p></dd>
 
 
-<dt>factor_name</dt>
+<dt id="arg-factor-name">factor_name<a class="anchor" aria-label="anchor" href="#arg-factor-name"></a></dt>
 <dd><p>(character) The name of a sample-meta column to use.</p></dd>
 
 
-<dt>factor_pred</dt>
+<dt id="arg-factor-pred">factor_pred<a class="anchor" aria-label="anchor" href="#arg-factor-pred"></a></dt>
 <dd><p>(data.frame) A data.frame, where each column is a factor of predicted group labels to compare with the true groups labels.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>fisher_exact</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>p_value</code></td><td>(data.frame) The probability of observing the calculated statistic if the null hypothesis is true.</td></tr><tr><td><code>significant</code></td><td>(data.frame) True/False indicating whether the p-value computed for each variable is less than the threshold.</td></tr></table></div>
+    <p>A  <code>fisher_exact</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>p_value</code></td><td>(data.frame) The probability of observing the calculated statistic if the null hypothesis is true.</td></tr><tr><td><code>significant</code></td><td>(data.frame) True/False indicating whether the p-value computed for each variable is less than the threshold.</td></tr></table></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>fisher_exact</code> object inherits the following <code>struct</code> classes: <br><br><code>[fisher_exact]</code> &gt;&gt; <code>[model]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -115,23 +103,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">M</span><span class="op">=</span><span class="fu"><a href="model_apply.html">model_apply</a></span><span class="op">(</span><span class="va">M</span>,<span class="va">D</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
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diff --git a/reference/fold_change.html b/reference/fold_change.html
index 45c1323..ee291cc 100644
--- a/reference/fold_change.html
+++ b/reference/fold_change.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
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-      <img src="" class="logo" alt=""><h1>Fold change</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/fold_change_class.R" class="external-link"><code>R/fold_change_class.R</code></a></small>
+
+      <h1>Fold change</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/fold_change_class.R" class="external-link"><code>R/fold_change_class.R</code></a></small>
       <div class="d-none name"><code>fold_change.Rd</code></div>
     </div>
 
@@ -68,51 +56,51 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>factor_name</dt>
+
+
+<dl><dt id="arg-factor-name">factor_name<a class="anchor" aria-label="anchor" href="#arg-factor-name"></a></dt>
 <dd><p>(character) The name of a sample-meta column to use.</p></dd>
 
 
-<dt>paired</dt>
+<dt id="arg-paired">paired<a class="anchor" aria-label="anchor" href="#arg-paired"></a></dt>
 <dd><p>(logical) Paired fold change. Allowed values are limited to the following:</p><ul><li><p><code>"TRUE"</code>: Fold change is calculated taking into account paired sampling.</p></li>
 <li><p><code>"FALSE"</code>: Fold change is calculated assuming there is no paired sampling.</p></li>
 </ul><p>The default is <code>FALSE</code>.<br></p></dd>
 
 
-<dt>sample_name</dt>
+<dt id="arg-sample-name">sample_name<a class="anchor" aria-label="anchor" href="#arg-sample-name"></a></dt>
 <dd><p>(character) The name of a sample_meta column containing sample identifiers for paired sampling. The default is <code>character(0)</code>.</p></dd>
 
 
-<dt>threshold</dt>
+<dt id="arg-threshold">threshold<a class="anchor" aria-label="anchor" href="#arg-threshold"></a></dt>
 <dd><p>(numeric) The fold change threshold for labelling features as significant. The default is <code>2</code>.<br></p></dd>
 
 
-<dt>control_group</dt>
+<dt id="arg-control-group">control_group<a class="anchor" aria-label="anchor" href="#arg-control-group"></a></dt>
 <dd><p>(character) The level name of the group used in the denominator (where possible) when computing fold change. The default is <code>character(0)</code>.</p></dd>
 
 
-<dt>method</dt>
+<dt id="arg-method">method<a class="anchor" aria-label="anchor" href="#arg-method"></a></dt>
 <dd><p>(character) Fold change method. Allowed values are limited to the following:</p><ul><li><p><code>"geometric"</code>: A log transform is applied before using group means to calculate fold change. In the non-tranformedspace this is equivalent to using geometric group means. Confidence intervals for independant and paired sampling are estimated using standard error of the mean in log transformed space before being transformed back to the original space.</p></li>
 <li><p><code>"median"</code>: The group medians and the method described by Price and Bonett is used to estimate confidence intervals. For paired data standard error of the median is used to estimate confidence intervals from the median fold change of all pairs.</p></li>
 <li><p><code>"mean"</code>: The group means and the method described by Price and Bonnet is used to estimate confidence intervals. For paired data standard error of the mean is used to estimate confidence intervals from the mean fold change of all pairs.</p></li>
 </ul><p>The default is <code>"geometric"</code>.</p></dd>
 
 
-<dt>conf_level</dt>
+<dt id="arg-conf-level">conf_level<a class="anchor" aria-label="anchor" href="#arg-conf-level"></a></dt>
 <dd><p>(numeric) The confidence level of the interval. The default is <code>0.95</code>.<br></p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>fold_change</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>fold_change</code></td><td>(data.frame) The fold change between groups.</td></tr><tr><td><code>lower_ci</code></td><td>(data.frame) Lower confidence interval for fold change.</td></tr><tr><td><code>upper_ci</code></td><td>(data.frame) Upper confidence interval for fold change.</td></tr><tr><td><code>significant</code></td><td>(data.frame) A logical indictor of whether the calculated fold change including the estimated confidence limits is greater than the selected threshold.</td></tr></table></div>
+    <p>A  <code>fold_change</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>fold_change</code></td><td>(data.frame) The fold change between groups.</td></tr><tr><td><code>lower_ci</code></td><td>(data.frame) Lower confidence interval for fold change.</td></tr><tr><td><code>upper_ci</code></td><td>(data.frame) Upper confidence interval for fold change.</td></tr><tr><td><code>significant</code></td><td>(data.frame) A logical indictor of whether the calculated fold change including the estimated confidence limits is greater than the selected threshold.</td></tr></table></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>fold_change</code> object inherits the following <code>struct</code> classes: <br><br><code>[fold_change]</code> &gt;&gt; <code>[model]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -139,23 +127,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="model_apply.html">model_apply</a></span><span class="op">(</span><span class="va">M</span>,<span class="va">D</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
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-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
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diff --git a/reference/fold_change_int.html b/reference/fold_change_int.html
index c7db449..9489e6f 100644
--- a/reference/fold_change_int.html
+++ b/reference/fold_change_int.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
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+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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 </nav><div class="container template-reference-topic">
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Fold change for interactions between factors</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/fold_change_int_class.R" class="external-link"><code>R/fold_change_int_class.R</code></a></small>
+
+      <h1>Fold change for interactions between factors</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/fold_change_int_class.R" class="external-link"><code>R/fold_change_int_class.R</code></a></small>
       <div class="d-none name"><code>fold_change_int.Rd</code></div>
     </div>
 
@@ -66,41 +54,41 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>factor_name</dt>
+
+
+<dl><dt id="arg-factor-name">factor_name<a class="anchor" aria-label="anchor" href="#arg-factor-name"></a></dt>
 <dd><p>(character) The name of a sample-meta column to use.</p></dd>
 
 
-<dt>threshold</dt>
+<dt id="arg-threshold">threshold<a class="anchor" aria-label="anchor" href="#arg-threshold"></a></dt>
 <dd><p>(numeric) The fold change threshold for labelling features as significant. The default is <code>2</code>.<br></p></dd>
 
 
-<dt>control_group</dt>
+<dt id="arg-control-group">control_group<a class="anchor" aria-label="anchor" href="#arg-control-group"></a></dt>
 <dd><p>(character) The level names of the groups used in the denominator (where possible) when computing fold change. One level for each factor, assumed to be in the same order as factor_name. The default is <code>character(0)</code>.</p></dd>
 
 
-<dt>method</dt>
+<dt id="arg-method">method<a class="anchor" aria-label="anchor" href="#arg-method"></a></dt>
 <dd><p>(character) Fold change method. Allowed values are limited to the following:</p><ul><li><p><code>"geometric"</code>: A log transform is applied before using group means to calculate fold change. In the non-tranformedspace this is equivalent to using geometric group means. Confidence intervals for independant and paired sampling are estimated using standard error of the mean in log transformed space before being transformed back to the original space.</p></li>
 <li><p><code>"median"</code>: The group medians and the method described by Price and Bonett is used to estimate confidence intervals. For paired data standard error of the median is used to estimate confidence intervals from the median fold change of all pairs.</p></li>
 <li><p><code>"mean"</code>: The group means and the method described by Price and Bonnet is used to estimate confidence intervals. For paired data standard error of the mean is used to estimate confidence intervals from the mean fold change of all pairs.</p></li>
 </ul><p>The default is <code>"geometric"</code>.</p></dd>
 
 
-<dt>conf_level</dt>
+<dt id="arg-conf-level">conf_level<a class="anchor" aria-label="anchor" href="#arg-conf-level"></a></dt>
 <dd><p>(numeric) The confidence level of the interval. The default is <code>0.95</code>.<br></p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>fold_change_int</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>fold_change</code></td><td>(data.frame) The fold change between groups.</td></tr><tr><td><code>lower_ci</code></td><td>(data.frame) Lower confidence interval for fold change.</td></tr><tr><td><code>upper_ci</code></td><td>(data.frame) Upper confidence interval for fold change.</td></tr><tr><td><code>significant</code></td><td>(data.frame) A logical indictor of whether the calculated fold change including the estimated confidence limits is greater than the selected threshold.</td></tr></table></div>
+    <p>A  <code>fold_change_int</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>fold_change</code></td><td>(data.frame) The fold change between groups.</td></tr><tr><td><code>lower_ci</code></td><td>(data.frame) Lower confidence interval for fold change.</td></tr><tr><td><code>upper_ci</code></td><td>(data.frame) Upper confidence interval for fold change.</td></tr><tr><td><code>significant</code></td><td>(data.frame) A logical indictor of whether the calculated fold change including the estimated confidence limits is greater than the selected threshold.</td></tr></table></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>fold_change_int</code> object inherits the following <code>struct</code> classes: <br><br><code>[fold_change_int]</code> &gt;&gt; <code>[fold_change]</code> &gt;&gt; <code>[model]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -130,23 +118,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="model_apply.html">model_apply</a></span><span class="op">(</span><span class="va">M</span>,<span class="va">D</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
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@@ -1,50 +1,38 @@
 <!DOCTYPE html>
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-      <img src="" class="logo" alt=""><h1>Fold change plot</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/fold_change_class.R" class="external-link"><code>R/fold_change_class.R</code></a></small>
+
+      <h1>Fold change plot</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/fold_change_class.R" class="external-link"><code>R/fold_change_class.R</code></a></small>
       <div class="d-none name"><code>fold_change_plot.Rd</code></div>
     </div>
 
@@ -59,33 +47,33 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>number_features</dt>
+
+
+<dl><dt id="arg-number-features">number_features<a class="anchor" aria-label="anchor" href="#arg-number-features"></a></dt>
 <dd><p>(numeric) The number randomly selected features to plot, or
 a list of column numbers. The default is <code>20</code>.<br></p></dd>
 
 
-<dt>orientation</dt>
+<dt id="arg-orientation">orientation<a class="anchor" aria-label="anchor" href="#arg-orientation"></a></dt>
 <dd><p>(character) Plot orientation. Allowed values are limited to the following:</p><ul><li><p><code>"landscape"</code>: Features are plotted on the y-axis.</p></li>
 <li><p><code>"portrait"</code>: Features are plotted on the x-axis.</p></li>
 </ul><p>The default is <code>"portrait"</code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>
+    <p>A  <code>
 fold_change_plot
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>fold_change_plot</code> object inherits the following <code>struct</code> classes: <br><br><code>[fold_change_plot]</code> &gt;&gt; <code>[chart]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -99,23 +87,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">fold_change_plot</span><span class="op">(</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
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-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
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+++ b/reference/forward_selection_by_rank.html
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-      <img src="" class="logo" alt=""><h1>Forward selection by rank</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/forward_selection_by_rank_class.R" class="external-link"><code>R/forward_selection_by_rank_class.R</code></a></small>
+
+      <h1>Forward selection by rank</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/forward_selection_by_rank_class.R" class="external-link"><code>R/forward_selection_by_rank_class.R</code></a></small>
       <div class="d-none name"><code>forward_selection_by_rank.Rd</code></div>
     </div>
 
@@ -66,38 +54,38 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>min_no_vars</dt>
+
+
+<dl><dt id="arg-min-no-vars">min_no_vars<a class="anchor" aria-label="anchor" href="#arg-min-no-vars"></a></dt>
 <dd><p>(numeric) The minimum number of variables to include in the model. The default is <code>1</code>.<br></p></dd>
 
 
-<dt>max_no_vars</dt>
+<dt id="arg-max-no-vars">max_no_vars<a class="anchor" aria-label="anchor" href="#arg-max-no-vars"></a></dt>
 <dd><p>(numeric) The maximum number of variables to include in the model. The default is <code>100</code>.<br></p></dd>
 
 
-<dt>step_size</dt>
+<dt id="arg-step-size">step_size<a class="anchor" aria-label="anchor" href="#arg-step-size"></a></dt>
 <dd><p>(numeric) The incremental change in number of features in the model. The default is <code>1</code>.<br></p></dd>
 
 
-<dt>factor_name</dt>
+<dt id="arg-factor-name">factor_name<a class="anchor" aria-label="anchor" href="#arg-factor-name"></a></dt>
 <dd><p>(character) The name of a sample-meta column to use.</p></dd>
 
 
-<dt>variable_rank</dt>
+<dt id="arg-variable-rank">variable_rank<a class="anchor" aria-label="anchor" href="#arg-variable-rank"></a></dt>
 <dd><p>(numeric, integer) The values used to rank the features.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>forward_selection_by_rank</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>metric</code></td><td>(data.frame) The value of the computed metric for each model. For nested models the metric is averaged.</td></tr><tr><td><code>results</code></td><td>(data.frame) The predicted outputs from collated from all models computed during forward selection.</td></tr><tr><td><code>chosen_vars</code></td><td>(numeric, integer) The column number of the variables chosen for the best performing model.</td></tr><tr><td><code>smoothed</code></td><td>(numeric) The value of the performance metric for each evaluated model after smoothing.</td></tr><tr><td><code>searchlist</code></td><td>(numeric) The maxmimum rank of features included in each model.</td></tr></table></div>
+    <p>A  <code>forward_selection_by_rank</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>metric</code></td><td>(data.frame) The value of the computed metric for each model. For nested models the metric is averaged.</td></tr><tr><td><code>results</code></td><td>(data.frame) The predicted outputs from collated from all models computed during forward selection.</td></tr><tr><td><code>chosen_vars</code></td><td>(numeric, integer) The column number of the variables chosen for the best performing model.</td></tr><tr><td><code>smoothed</code></td><td>(numeric) The value of the performance metric for each evaluated model after smoothing.</td></tr><tr><td><code>searchlist</code></td><td>(numeric) The maxmimum rank of features included in each model.</td></tr></table></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>forward_selection_by_rank</code> object inherits the following <code>struct</code> classes: <br><br><code>[forward_selection_by_rank]</code> &gt;&gt; <code>[resampler]</code> &gt;&gt; <code>[iterator]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -133,23 +121,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
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diff --git a/reference/fs_line-1.png b/reference/fs_line-1.png
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+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Forward selection line plot</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/forward_selection_by_rank_class.R" class="external-link"><code>R/forward_selection_by_rank_class.R</code></a></small>
+
+      <h1>Forward selection line plot</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/forward_selection_by_rank_class.R" class="external-link"><code>R/forward_selection_by_rank_class.R</code></a></small>
       <div class="d-none name"><code>fs_line.Rd</code></div>
     </div>
 
@@ -59,22 +47,22 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>...</dt>
+
+
+<dl><dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>
+    <p>A  <code>
 fs_line
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>fs_line</code> object inherits the following <code>struct</code> classes: <br><br><code>[fs_line]</code> &gt;&gt; <code>[chart]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -110,23 +98,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/glog_opt_plot-1.png b/reference/glog_opt_plot-1.png
index ca49599..19ab5a2 100644
Binary files a/reference/glog_opt_plot-1.png and b/reference/glog_opt_plot-1.png differ
diff --git a/reference/glog_opt_plot.html b/reference/glog_opt_plot.html
index fb4f4e7..c5360b0 100644
--- a/reference/glog_opt_plot.html
+++ b/reference/glog_opt_plot.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="A plot of the sum of squares error (SSE) vs different values of lambda for the glog transform. The indicated optimum value for lambda minimises the SSE."><title>Glog optimisation — glog_opt_plot • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Glog optimisation — glog_opt_plot"><meta property="og:description" content="A plot of the sum of squares error (SSE) vs different values of lambda for the glog transform. The indicated optimum value for lambda minimises the SSE."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Glog optimisation — glog_opt_plot • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Glog optimisation — glog_opt_plot"><meta name="description" content="A plot of the sum of squares error (SSE) vs different values of lambda for the glog transform. The indicated optimum value for lambda minimises the SSE."><meta property="og:description" content="A plot of the sum of squares error (SSE) vs different values of lambda for the glog transform. The indicated optimum value for lambda minimises the SSE."></head><body>
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+
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.17.3</small>
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
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+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
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   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Glog optimisation</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/glog_class.R" class="external-link"><code>R/glog_class.R</code></a></small>
+
+      <h1>Glog optimisation</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/glog_class.R" class="external-link"><code>R/glog_class.R</code></a></small>
       <div class="d-none name"><code>glog_opt_plot.Rd</code></div>
     </div>
 
@@ -59,19 +47,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>plot_grid</dt>
+
+
+<dl><dt id="arg-plot-grid">plot_grid<a class="anchor" aria-label="anchor" href="#arg-plot-grid"></a></dt>
 <dd><p>(numeric)  The default is <code>100</code>.<br></p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>
+    <p>A  <code>
 glog_opt_plot
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
@@ -82,16 +70,15 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 </ul></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>glog_opt_plot</code> object inherits the following <code>struct</code> classes: <br><br><code>[glog_opt_plot]</code> &gt;&gt; <code>[chart]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
     <div class="section level2">
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
-    <p>Jankevics A, Lloyd GR, Weber RJM (2023). <em>pmp: Peak Matrix Processing
-and signal batch correction for metabolomics datasets</em>.
-doi:10.18129/B9.bioc.pmp <a href="https://doi.org/10.18129/B9.bioc.pmp" class="external-link">https://doi.org/10.18129/B9.bioc.pmp</a>, R
-package version 1.14.0, <a href="https://bioconductor.org/packages/pmp" class="external-link">https://bioconductor.org/packages/pmp</a>.</p>
+    <p>Jankevics A, Lloyd GR, Weber RJM (????). <em>pmp: Peak Matrix Processing
+and signal batch correction for metabolomics datasets</em>. R package
+version 1.15.1.</p>
     </div>
 
     <div class="section level2">
@@ -109,23 +96,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-plt img"><img src="glog_opt_plot-1.png" alt="" width="700" height="433"></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
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   </body></html>
 
diff --git a/reference/glog_transform.html b/reference/glog_transform.html
index 4d472a2..a4d38f1 100644
--- a/reference/glog_transform.html
+++ b/reference/glog_transform.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="The generalised logarithm (glog) transformation applies a log transformation while applying an offset to account for technical variation."><title>Generalised logarithmic transform — glog_transform • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Generalised logarithmic transform — glog_transform"><meta property="og:description" content="The generalised logarithm (glog) transformation applies a log transformation while applying an offset to account for technical variation."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Generalised logarithmic transform — glog_transform • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Generalised logarithmic transform — glog_transform"><meta name="description" content="The generalised logarithm (glog) transformation applies a log transformation while applying an offset to account for technical variation."><meta property="og:description" content="The generalised logarithm (glog) transformation applies a log transformation while applying an offset to account for technical variation."></head><body>
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-      <img src="" class="logo" alt=""><h1>Generalised logarithmic transform</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/glog_class.R" class="external-link"><code>R/glog_class.R</code></a></small>
+
+      <h1>Generalised logarithmic transform</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/glog_class.R" class="external-link"><code>R/glog_class.R</code></a></small>
       <div class="d-none name"><code>glog_transform.Rd</code></div>
     </div>
 
@@ -59,43 +47,42 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>qc_label</dt>
+
+
+<dl><dt id="arg-qc-label">qc_label<a class="anchor" aria-label="anchor" href="#arg-qc-label"></a></dt>
 <dd><p>(character) The label used to identify QC samples. The default is <code>"QC"</code>.</p></dd>
 
 
-<dt>factor_name</dt>
+<dt id="arg-factor-name">factor_name<a class="anchor" aria-label="anchor" href="#arg-factor-name"></a></dt>
 <dd><p>(character) The name of a sample-meta column to use.</p></dd>
 
 
-<dt>lambda</dt>
+<dt id="arg-lambda">lambda<a class="anchor" aria-label="anchor" href="#arg-lambda"></a></dt>
 <dd><p>(numeric, NULL) The value of lambda to use. If NULL then the pmp package will be used to determine an "optimal" value for lambda. The default is <code>NULL</code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>glog_transform</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>transformed</code></td><td>(DatasetExperiment) A DatasetExperiment object containing the glog transformed data.</td></tr><tr><td><code>error_flag</code></td><td>(logical) A logical indicating whether the glog optimisation for lambda was successful. If not then PMP returns a default value for lambda.</td></tr></table></div>
+    <p>A  <code>glog_transform</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>transformed</code></td><td>(DatasetExperiment) A DatasetExperiment object containing the glog transformed data.</td></tr><tr><td><code>error_flag</code></td><td>(logical) A logical indicating whether the glog optimisation for lambda was successful. If not then PMP returns a default value for lambda.</td></tr></table></div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>pmp</code></p></li>
 </ul></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>glog_transform</code> object inherits the following <code>struct</code> classes: <br><br><code>[glog_transform]</code> &gt;&gt; <code>[model]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
     <div class="section level2">
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
-    <p>Jankevics A, Lloyd GR, Weber RJM (2023). <em>pmp: Peak Matrix Processing
-and signal batch correction for metabolomics datasets</em>.
-doi:10.18129/B9.bioc.pmp <a href="https://doi.org/10.18129/B9.bioc.pmp" class="external-link">https://doi.org/10.18129/B9.bioc.pmp</a>, R
-package version 1.14.0, <a href="https://bioconductor.org/packages/pmp" class="external-link">https://bioconductor.org/packages/pmp</a>.</p>
+    <p>Jankevics A, Lloyd GR, Weber RJM (????). <em>pmp: Peak Matrix Processing
+and signal batch correction for metabolomics datasets</em>. R package
+version 1.15.1.</p>
 <p>Durbin B, Hardin J, Hawkins D, Rocke D (2002). "A variance-stabilizing
 transformation for gene-expression microarray data." <em>Bioinformatics</em>,
 <em>18</em>(Suppl 1), S105-S110.</p>
@@ -119,23 +106,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> Error!Lambda tending to infinity! Using standard</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
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index f9eecdf..3b0dbd7 100644
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+++ b/reference/grid_search_1d.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>One dimensional grid search — grid_search_1d • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="One dimensional grid search — grid_search_1d"><meta name="description" content='A one dimensional grid search calculates a performance metric for a model at evenly spaced values for a model input parameter. The "optimum" value for the parameter is suggested as the one which maximises performance, or minimises error (whichever is appropriate for the chosen metric)'><meta property="og:description" content='A one dimensional grid search calculates a performance metric for a model at evenly spaced values for a model input parameter. The "optimum" value for the parameter is suggested as the one which maximises performance, or minimises error (whichever is appropriate for the chosen metric)'></head><body>
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     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
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+
 
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
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+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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 </nav><div class="container template-reference-topic">
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>One dimensional grid search</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/grid_search_1d_class.R" class="external-link"><code>R/grid_search_1d_class.R</code></a></small>
+
+      <h1>One dimensional grid search</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/grid_search_1d_class.R" class="external-link"><code>R/grid_search_1d_class.R</code></a></small>
       <div class="d-none name"><code>grid_search_1d.Rd</code></div>
     </div>
 
@@ -66,40 +54,40 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>param_to_optimise</dt>
+
+
+<dl><dt id="arg-param-to-optimise">param_to_optimise<a class="anchor" aria-label="anchor" href="#arg-param-to-optimise"></a></dt>
 <dd><p>(character) The name of the model input parameter that is the focus of the search.</p></dd>
 
 
-<dt>search_values</dt>
+<dt id="arg-search-values">search_values<a class="anchor" aria-label="anchor" href="#arg-search-values"></a></dt>
 <dd><p>(ANY) The values of the input parameter being optimised.</p></dd>
 
 
-<dt>model_index</dt>
+<dt id="arg-model-index">model_index<a class="anchor" aria-label="anchor" href="#arg-model-index"></a></dt>
 <dd><p>(numeric, integer) The index of the model in the sequence that uses the parameter being optimised.</p></dd>
 
 
-<dt>factor_name</dt>
+<dt id="arg-factor-name">factor_name<a class="anchor" aria-label="anchor" href="#arg-factor-name"></a></dt>
 <dd><p>(character) The name of a sample-meta column to use.</p></dd>
 
 
-<dt>max_min</dt>
+<dt id="arg-max-min">max_min<a class="anchor" aria-label="anchor" href="#arg-max-min"></a></dt>
 <dd><p>(character) Maximise or minimise. Allowed values are limited to the following:</p><ul><li><p><code>"max"</code>: The optimium parameter value is suggested based on maximising the performance metric.</p></li>
 <li><p><code>"min"</code>: The optimium parameter value is suggested based on minimising the performance metric.</p></li>
 </ul><p>The default is <code>"min"</code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>grid_search_1d</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>results</code></td><td>(data.frame)</td></tr><tr><td><code>metric</code></td><td>(data.frame)</td></tr><tr><td><code>optimum_value</code></td><td>(numeric)</td></tr></table></div>
+    <p>A  <code>grid_search_1d</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>results</code></td><td>(data.frame)</td></tr><tr><td><code>metric</code></td><td>(data.frame)</td></tr><tr><td><code>optimum_value</code></td><td>(numeric)</td></tr></table></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>grid_search_1d</code> object inherits the following <code>struct</code> classes: <br><br><code>[grid_search_1d]</code> &gt;&gt; <code>[resampler]</code> &gt;&gt; <code>[iterator]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -133,23 +121,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
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diff --git a/reference/gs_line.html b/reference/gs_line.html
index 957f8c3..10464ca 100644
--- a/reference/gs_line.html
+++ b/reference/gs_line.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="A plot of the calculated performance metric against the model input parameter values used to train the model. The optimum parameter value is indicated based on minimising (or maximising) the chosen metric."><title>Grid search line plot — gs_line • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Grid search line plot — gs_line"><meta property="og:description" content="A plot of the calculated performance metric against the model input parameter values used to train the model. The optimum parameter value is indicated based on minimising (or maximising) the chosen metric."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Grid search line plot — gs_line • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Grid search line plot — gs_line"><meta name="description" content="A plot of the calculated performance metric against the model input parameter values used to train the model. The optimum parameter value is indicated based on minimising (or maximising) the chosen metric."><meta property="og:description" content="A plot of the calculated performance metric against the model input parameter values used to train the model. The optimum parameter value is indicated based on minimising (or maximising) the chosen metric."></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary"><div class="container">
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+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.17.3</small>
+
 
-    
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     <div id="navbar" class="collapse navbar-collapse ms-3">
-      <ul class="navbar-nav me-auto"><li class="active nav-item">
-  <a class="nav-link" href="../reference/index.html">Reference</a>
-</li>
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
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-    <a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a>
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-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
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-     
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Grid search line plot</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/grid_search_1d_class.R" class="external-link"><code>R/grid_search_1d_class.R</code></a></small>
+
+      <h1>Grid search line plot</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/grid_search_1d_class.R" class="external-link"><code>R/grid_search_1d_class.R</code></a></small>
       <div class="d-none name"><code>gs_line.Rd</code></div>
     </div>
 
@@ -59,22 +47,22 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>...</dt>
+
+
+<dl><dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>
+    <p>A  <code>
 gs_line
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>gs_line</code> object inherits the following <code>struct</code> classes: <br><br><code>[gs_line]</code> &gt;&gt; <code>[chart]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -86,23 +74,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">gs_line</span><span class="op">(</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/hca_dendrogram.html b/reference/hca_dendrogram.html
index 7dcb65b..2df036e 100644
--- a/reference/hca_dendrogram.html
+++ b/reference/hca_dendrogram.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="A dendrogram visualising the clustering by HCA."><title>HCA dendrogram — hca_dendrogram • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="HCA dendrogram — hca_dendrogram"><meta property="og:description" content="A dendrogram visualising the clustering by HCA."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>HCA dendrogram — hca_dendrogram • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="HCA dendrogram — hca_dendrogram"><meta name="description" content="A dendrogram visualising the clustering by HCA."><meta property="og:description" content="A dendrogram visualising the clustering by HCA."></head><body>
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.17.3</small>
+
 
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+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>HCA dendrogram</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/hca_class.R" class="external-link"><code>R/hca_class.R</code></a></small>
+
+      <h1>HCA dendrogram</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/hca_class.R" class="external-link"><code>R/hca_class.R</code></a></small>
       <div class="d-none name"><code>hca_dendrogram.Rd</code></div>
     </div>
 
@@ -59,15 +47,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>...</dt>
+
+
+<dl><dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>
+    <p>A  <code>
 hca_dendrogram
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
@@ -78,14 +66,14 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 </ul></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>hca_dendrogram</code> object inherits the following <code>struct</code> classes: <br><br><code>[hca_dendrogram]</code> &gt;&gt; <code>[chart]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
     <div class="section level2">
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
-    <p>de Vries A, Ripley BD (2022). <em>ggdendro: Create Dendrograms and Tree
-Diagrams Using 'ggplot2'</em>. R package version 0.1.23,
+    <p>de Vries A, Ripley BD (2024). <em>ggdendro: Create Dendrograms and Tree
+Diagrams Using 'ggplot2'</em>. R package version 0.2.0,
 <a href="https://CRAN.R-project.org/package=ggdendro" class="external-link">https://CRAN.R-project.org/package=ggdendro</a>.</p>
     </div>
 
@@ -96,23 +84,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">hca_dendrogram</span><span class="op">(</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
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-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
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diff --git a/reference/index.html b/reference/index.html
index 847a636..e36ba43 100644
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     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.17.3</small>
+
 
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-      <img src="" class="logo" alt=""><h1>Function reference</h1>
+
+      <h1>Package index</h1>
     </div>
 
     <div class="section level2">
       <h2 id="all-functions">All functions<a class="anchor" aria-label="anchor" href="#all-functions"></a></h2>
-      
-      
 
-      
-    </div><div id="" class="section level2">
-      
-      
-      
+
+
+
+    </div><div class="section level2">
+
+
+
 
       <dl><dt>
-          
-          <code><a href="ANOVA.html">ANOVA()</a></code> 
+
+          <code><a href="ANOVA.html">ANOVA()</a></code>
+
         </dt>
         <dd>Analysis of Variance</dd>
       </dl><dl><dt>
-          
-          <code><a href="AUC.html">AUC()</a></code> 
+
+          <code><a href="AUC.html">AUC()</a></code>
+
         </dt>
         <dd>Area under ROC curve</dd>
       </dl><dl><dt>
-          
-          <code><a href="DFA.html">DFA()</a></code> 
+
+          <code><a href="DFA.html">DFA()</a></code>
+
         </dt>
         <dd>Discriminant Factor Analysis</dd>
       </dl><dl><dt>
-          
-          <code><a href="DatasetExperiment_boxplot.html">DatasetExperiment_boxplot()</a></code> 
+
+          <code><a href="DatasetExperiment_boxplot.html">DatasetExperiment_boxplot()</a></code>
+
         </dt>
         <dd>Feature distribution histogram</dd>
       </dl><dl><dt>
-          
-          <code><a href="DatasetExperiment_dist.html">DatasetExperiment_dist()</a></code> 
+
+          <code><a href="DatasetExperiment_dist.html">DatasetExperiment_dist()</a></code>
+
         </dt>
         <dd>Feature distribution histogram</dd>
       </dl><dl><dt>
-          
-          <code><a href="DatasetExperiment_factor_boxplot.html">DatasetExperiment_factor_boxplot()</a></code> 
+
+          <code><a href="DatasetExperiment_factor_boxplot.html">DatasetExperiment_factor_boxplot()</a></code>
+
         </dt>
         <dd>Factor boxplot</dd>
       </dl><dl><dt>
-          
-          <code><a href="DatasetExperiment_heatmap.html">DatasetExperiment_heatmap()</a></code> 
+
+          <code><a href="DatasetExperiment_heatmap.html">DatasetExperiment_heatmap()</a></code>
+
         </dt>
         <dd>DatasetExperiment heatmap</dd>
       </dl><dl><dt>
-          
-          <code><a href="HCA.html">HCA()</a></code> 
+
+          <code><a href="HCA.html">HCA()</a></code>
+
         </dt>
         <dd>Hierarchical Cluster Analysis</dd>
       </dl><dl><dt>
-          
-          <code><a href="HSD.html">HSD()</a></code> 
+
+          <code><a href="HSD.html">HSD()</a></code>
+
         </dt>
         <dd>Tukey's Honest Significant Difference</dd>
       </dl><dl><dt>
-          
-          <code><a href="HSDEM.html">HSDEM()</a></code> 
+
+          <code><a href="HSDEM.html">HSDEM()</a></code>
+
         </dt>
         <dd>Tukey's Honest Significant Difference using estimated marginal means</dd>
       </dl><dl><dt>
-          
-          <code><a href="MTBLS79_DatasetExperiment.html">MTBLS79_DatasetExperiment()</a></code> 
+
+          <code><a href="MTBLS79_DatasetExperiment.html">MTBLS79_DatasetExperiment()</a></code>
+
         </dt>
         <dd>MTBLS79: Direct infusion mass spectrometry metabolomics dataset: a benchmark for data processing and quality control</dd>
       </dl><dl><dt>
-          
-          <code><a href="OPLSDA.html">OPLSDA()</a></code> 
+
+          <code><a href="OPLSDA.html">OPLSDA()</a></code>
+
         </dt>
         <dd>Orthogonal Partial Least Squares regression</dd>
       </dl><dl><dt>
-          
-          <code><a href="OPLSR.html">OPLSR()</a></code> 
+
+          <code><a href="OPLSR.html">OPLSR()</a></code>
+
         </dt>
         <dd>Orthogonal Partial Least Squares regression</dd>
       </dl><dl><dt>
-          
-          <code><a href="PCA.html">PCA()</a></code> 
+
+          <code><a href="PCA.html">PCA()</a></code>
+
         </dt>
         <dd>Principal Component Analysis (PCA)</dd>
       </dl><dl><dt>
-          
-          <code><a href="PLSDA.html">PLSDA()</a></code> 
+
+          <code><a href="PLSDA.html">PLSDA()</a></code>
+
         </dt>
         <dd>Partial least squares discriminant analysis</dd>
       </dl><dl><dt>
-          
-          <code><a href="PLSR.html">PLSR()</a></code> 
+
+          <code><a href="PLSR.html">PLSR()</a></code>
+
         </dt>
         <dd>Partial least squares regression</dd>
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-          
-          <code><a href="SVM.html">SVM()</a></code> 
+
+          <code><a href="SVM.html">SVM()</a></code>
+
         </dt>
         <dd>Support Vector Machine Classifier</dd>
       </dl><dl><dt>
-          
-          <code><a href="as_data_frame.html">as_data_frame(<i>&lt;filter_na_count&gt;</i>)</a></code> <code><a href="as_data_frame.html">as_data_frame(<i>&lt;ttest&gt;</i>)</a></code> <code><a href="as_data_frame.html">as_data_frame(<i>&lt;wilcox_test&gt;</i>)</a></code> 
+
+          <code><a href="as_data_frame.html">as_data_frame(<i>&lt;filter_na_count&gt;</i>)</a></code> <code><a href="as_data_frame.html">as_data_frame(<i>&lt;ttest&gt;</i>)</a></code> <code><a href="as_data_frame.html">as_data_frame(<i>&lt;wilcox_test&gt;</i>)</a></code>
+
         </dt>
         <dd>Convert to data.frame</dd>
       </dl><dl><dt>
-          
-          <code><a href="autoscale.html">autoscale()</a></code> 
+
+          <code><a href="autoscale.html">autoscale()</a></code>
+
         </dt>
         <dd>Autoscaling</dd>
       </dl><dl><dt>
-          
-          <code><a href="balanced_accuracy.html">balanced_accuracy()</a></code> 
+
+          <code><a href="balanced_accuracy.html">balanced_accuracy()</a></code>
+
         </dt>
         <dd>Balanced Accuracy</dd>
       </dl><dl><dt>
-          
-          <code><a href="blank_filter.html">blank_filter()</a></code> 
+
+          <code><a href="blank_filter.html">blank_filter()</a></code>
+
         </dt>
         <dd>Blank filter</dd>
       </dl><dl><dt>
-          
-          <code><a href="blank_filter_hist.html">blank_filter_hist()</a></code> 
+
+          <code><a href="blank_filter_hist.html">blank_filter_hist()</a></code>
+
         </dt>
         <dd>Histogram of blank filter fold changes</dd>
       </dl><dl><dt>
-          
-          <code><a href="bootstrap.html">bootstrap()</a></code> 
+
+          <code><a href="bootstrap.html">bootstrap()</a></code>
+
         </dt>
         <dd>Bootstrap resampling</dd>
       </dl><dl><dt>
-          
-          <code><a href="calculate.html">calculate(<i>&lt;AUC&gt;</i>)</a></code> <code><a href="calculate.html">calculate(<i>&lt;balanced_accuracy&gt;</i>)</a></code> <code><a href="calculate.html">calculate(<i>&lt;r_squared&gt;</i>)</a></code> 
+
+          <code><a href="calculate.html">calculate(<i>&lt;AUC&gt;</i>)</a></code> <code><a href="calculate.html">calculate(<i>&lt;balanced_accuracy&gt;</i>)</a></code> <code><a href="calculate.html">calculate(<i>&lt;r_squared&gt;</i>)</a></code>
+
         </dt>
         <dd>Calculate metric</dd>
       </dl><dl><dt>
-          
-          <code><a href="chart_plot.html">chart_plot(<i>&lt;dfa_scores_plot&gt;</i>,<i>&lt;DFA&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;scatter_chart&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;pca_correlation_plot&gt;</i>,<i>&lt;PCA&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;pca_scores_plot&gt;</i>,<i>&lt;PCA&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;pca_biplot&gt;</i>,<i>&lt;PCA&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;pca_loadings_plot&gt;</i>,<i>&lt;PCA&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;pca_scree_plot&gt;</i>,<i>&lt;PCA&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;pca_dstat_plot&gt;</i>,<i>&lt;PCA&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;plsr_prediction_plot&gt;</i>,<i>&lt;PLSR&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;plsr_residual_hist&gt;</i>,<i>&lt;PLSR&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;plsr_qq_plot&gt;</i>,<i>&lt;PLSR&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;plsr_cook_dist&gt;</i>,<i>&lt;PLSR&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;pls_scores_plot&gt;</i>,<i>&lt;PLSR&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;plsda_predicted_plot&gt;</i>,<i>&lt;PLSDA&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;plsda_roc_plot&gt;</i>,<i>&lt;PLSDA&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;pls_vip_plot&gt;</i>,<i>&lt;PLSR&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;pls_regcoeff_plot&gt;</i>,<i>&lt;PLSR&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;blank_filter_hist&gt;</i>,<i>&lt;blank_filter&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;confounders_lsq_barchart&gt;</i>,<i>&lt;confounders_clsq&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;confounders_lsq_boxplot&gt;</i>,<i>&lt;confounders_clsq&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;feature_boxplot&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;mv_histogram&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;mv_boxplot&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;DatasetExperiment_dist&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;DatasetExperiment_boxplot&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;compare_dist&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;DatasetExperiment_heatmap&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;DatasetExperiment_factor_boxplot&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;feature_profile_array&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;feature_profile&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;fold_change_plot&gt;</i>,<i>&lt;fold_change&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;fs_line&gt;</i>,<i>&lt;forward_selection_by_rank&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;glog_opt_plot&gt;</i>,<i>&lt;glog_transform&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;gs_line&gt;</i>,<i>&lt;grid_search_1d&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;hca_dendrogram&gt;</i>,<i>&lt;HCA&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;kfoldxcv_grid&gt;</i>,<i>&lt;kfold_xval&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;kfoldxcv_metric&gt;</i>,<i>&lt;kfold_xval&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;kw_p_hist&gt;</i>,<i>&lt;kw_rank_sum&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;mv_feature_filter_hist&gt;</i>,<i>&lt;mv_feature_filter&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;mv_sample_filter_hist&gt;</i>,<i>&lt;mv_sample_filter&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;permutation_test_plot&gt;</i>,<i>&lt;permutation_test&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;plsda_feature_importance_plot&gt;</i>,<i>&lt;PLSDA&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;pqn_norm_hist&gt;</i>,<i>&lt;pqn_norm&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;resample_chart&gt;</i>,<i>&lt;resample&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;rsd_filter_hist&gt;</i>,<i>&lt;rsd_filter&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;feature_profile&gt;</i>,<i>&lt;sb_corr&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;svm_plot_2d&gt;</i>,<i>&lt;SVM&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;tSNE_scatter&gt;</i>,<i>&lt;tSNE&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;tic_chart&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;wilcox_p_hist&gt;</i>,<i>&lt;wilcox_test&gt;</i>)</a></code> 
+
+          <code><a href="chart_plot.html">chart_plot(<i>&lt;dfa_scores_plot&gt;</i>,<i>&lt;DFA&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;scatter_chart&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;pca_correlation_plot&gt;</i>,<i>&lt;PCA&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;pca_scores_plot&gt;</i>,<i>&lt;PCA&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;pca_biplot&gt;</i>,<i>&lt;PCA&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;pca_loadings_plot&gt;</i>,<i>&lt;PCA&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;pca_scree_plot&gt;</i>,<i>&lt;PCA&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;pca_dstat_plot&gt;</i>,<i>&lt;PCA&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;plsr_prediction_plot&gt;</i>,<i>&lt;PLSR&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;plsr_residual_hist&gt;</i>,<i>&lt;PLSR&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;plsr_qq_plot&gt;</i>,<i>&lt;PLSR&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;plsr_cook_dist&gt;</i>,<i>&lt;PLSR&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;pls_scores_plot&gt;</i>,<i>&lt;PLSR&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;plsda_predicted_plot&gt;</i>,<i>&lt;PLSDA&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;plsda_roc_plot&gt;</i>,<i>&lt;PLSDA&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;pls_vip_plot&gt;</i>,<i>&lt;PLSR&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;pls_regcoeff_plot&gt;</i>,<i>&lt;PLSR&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;blank_filter_hist&gt;</i>,<i>&lt;blank_filter&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;confounders_lsq_barchart&gt;</i>,<i>&lt;confounders_clsq&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;confounders_lsq_boxplot&gt;</i>,<i>&lt;confounders_clsq&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;feature_boxplot&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;mv_histogram&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;mv_boxplot&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;DatasetExperiment_dist&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;DatasetExperiment_boxplot&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;compare_dist&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;DatasetExperiment_heatmap&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;DatasetExperiment_factor_boxplot&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;feature_profile_array&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;feature_profile&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;fold_change_plot&gt;</i>,<i>&lt;fold_change&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;fs_line&gt;</i>,<i>&lt;forward_selection_by_rank&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;glog_opt_plot&gt;</i>,<i>&lt;glog_transform&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;gs_line&gt;</i>,<i>&lt;grid_search_1d&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;hca_dendrogram&gt;</i>,<i>&lt;HCA&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;kfoldxcv_grid&gt;</i>,<i>&lt;kfold_xval&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;kfoldxcv_metric&gt;</i>,<i>&lt;kfold_xval&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;kw_p_hist&gt;</i>,<i>&lt;kw_rank_sum&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;mv_feature_filter_hist&gt;</i>,<i>&lt;mv_feature_filter&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;mv_sample_filter_hist&gt;</i>,<i>&lt;mv_sample_filter&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;permutation_test_plot&gt;</i>,<i>&lt;permutation_test&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;plsda_feature_importance_plot&gt;</i>,<i>&lt;PLSDA&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;pqn_norm_hist&gt;</i>,<i>&lt;pqn_norm&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;resample_chart&gt;</i>,<i>&lt;resample&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;rsd_filter_hist&gt;</i>,<i>&lt;rsd_filter&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;feature_profile&gt;</i>,<i>&lt;sb_corr&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;svm_plot_2d&gt;</i>,<i>&lt;SVM&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;tSNE_scatter&gt;</i>,<i>&lt;tSNE&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;tic_chart&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="chart_plot.html">chart_plot(<i>&lt;wilcox_p_hist&gt;</i>,<i>&lt;wilcox_test&gt;</i>)</a></code>
+
         </dt>
         <dd>chart_plot method</dd>
       </dl><dl><dt>
-          
-          <code><a href="classical_lsq.html">classical_lsq()</a></code> 
+
+          <code><a href="classical_lsq.html">classical_lsq()</a></code>
+
         </dt>
         <dd>Univariate Classical Least Squares Regression</dd>
       </dl><dl><dt>
-          
-          <code><a href="compare_dist.html">compare_dist()</a></code> 
+
+          <code><a href="compare_dist.html">compare_dist()</a></code>
+
         </dt>
         <dd>Compare distributions</dd>
       </dl><dl><dt>
-          
-          <code><a href="confounders_clsq.html">confounders_clsq()</a></code> 
+
+          <code><a href="confounders_clsq.html">confounders_clsq()</a></code>
+
         </dt>
         <dd>Check for confounding factors</dd>
       </dl><dl><dt>
-          
-          <code><a href="confounders_lsq_barchart.html">confounders_lsq_barchart()</a></code> 
+
+          <code><a href="confounders_lsq_barchart.html">confounders_lsq_barchart()</a></code>
+
         </dt>
         <dd>Confounding factor relative change barchart</dd>
       </dl><dl><dt>
-          
-          <code><a href="confounders_lsq_boxplot.html">confounders_lsq_boxplot()</a></code> 
+
+          <code><a href="confounders_lsq_boxplot.html">confounders_lsq_boxplot()</a></code>
+
         </dt>
         <dd>Confounding factor relative change boxplot</dd>
       </dl><dl><dt>
-          
-          <code><a href="constant_sum_norm.html">constant_sum_norm()</a></code> 
+
+          <code><a href="constant_sum_norm.html">constant_sum_norm()</a></code>
+
         </dt>
         <dd>Normalisation to constant sum</dd>
       </dl><dl><dt>
-          
-          <code><a href="corr_coef.html">corr_coef()</a></code> 
+
+          <code><a href="corr_coef.html">corr_coef()</a></code>
+
         </dt>
         <dd>Correlation coefficient</dd>
       </dl><dl><dt>
-          
-          <code><a href="dfa_scores_plot.html">dfa_scores_plot()</a></code> 
+
+          <code><a href="dfa_scores_plot.html">dfa_scores_plot()</a></code>
+
         </dt>
         <dd>DFA scores plot</dd>
       </dl><dl><dt>
-          
-          <code><a href="dratio_filter.html">dratio_filter()</a></code> 
+
+          <code><a href="dratio_filter.html">dratio_filter()</a></code>
+
         </dt>
         <dd>Dispersion ratio filter</dd>
       </dl><dl><dt>
-          
-          <code><a href="equal_split.html">equal_split()</a></code> 
+
+          <code><a href="equal_split.html">equal_split()</a></code>
+
         </dt>
         <dd>Equal group sized sampling</dd>
       </dl><dl><dt>
-          
-          <code><a href="feature_boxplot.html">feature_boxplot()</a></code> 
+
+          <code><a href="feature_boxplot.html">feature_boxplot()</a></code>
+
         </dt>
         <dd>Feature boxplot</dd>
       </dl><dl><dt>
-          
-          <code><a href="feature_profile.html">feature_profile()</a></code> 
+
+          <code><a href="feature_profile.html">feature_profile()</a></code>
+
         </dt>
         <dd>Feature profile</dd>
       </dl><dl><dt>
-          
-          <code><a href="feature_profile_array.html">feature_profile_array()</a></code> 
+
+          <code><a href="feature_profile_array.html">feature_profile_array()</a></code>
+
         </dt>
         <dd>Feature profile</dd>
       </dl><dl><dt>
-          
-          <code><a href="filter_by_name.html">filter_by_name()</a></code> 
+
+          <code><a href="filter_by_name.html">filter_by_name()</a></code>
+
         </dt>
         <dd>Filter by name</dd>
       </dl><dl><dt>
-          
-          <code><a href="filter_na_count.html">filter_na_count()</a></code> 
+
+          <code><a href="filter_na_count.html">filter_na_count()</a></code>
+
         </dt>
         <dd>Minimum number of measured values filter</dd>
       </dl><dl><dt>
-          
-          <code><a href="filter_smeta.html">filter_smeta()</a></code> 
+
+          <code><a href="filter_smeta.html">filter_smeta()</a></code>
+
         </dt>
         <dd>Filter by sample meta data</dd>
       </dl><dl><dt>
-          
-          <code><a href="fisher_exact.html">fisher_exact()</a></code> 
+
+          <code><a href="fisher_exact.html">fisher_exact()</a></code>
+
         </dt>
         <dd>Fisher Exact Test</dd>
       </dl><dl><dt>
-          
-          <code><a href="fold_change.html">fold_change()</a></code> 
+
+          <code><a href="fold_change.html">fold_change()</a></code>
+
         </dt>
         <dd>Fold change</dd>
       </dl><dl><dt>
-          
-          <code><a href="fold_change_int.html">fold_change_int()</a></code> 
+
+          <code><a href="fold_change_int.html">fold_change_int()</a></code>
+
         </dt>
         <dd>Fold change for interactions between factors</dd>
       </dl><dl><dt>
-          
-          <code><a href="fold_change_plot.html">fold_change_plot()</a></code> 
+
+          <code><a href="fold_change_plot.html">fold_change_plot()</a></code>
+
         </dt>
         <dd>Fold change plot</dd>
       </dl><dl><dt>
-          
-          <code><a href="forward_selection_by_rank.html">forward_selection_by_rank()</a></code> 
+
+          <code><a href="forward_selection_by_rank.html">forward_selection_by_rank()</a></code>
+
         </dt>
         <dd>Forward selection by rank</dd>
       </dl><dl><dt>
-          
-          <code><a href="fs_line.html">fs_line()</a></code> 
+
+          <code><a href="fs_line.html">fs_line()</a></code>
+
         </dt>
         <dd>Forward selection line plot</dd>
       </dl><dl><dt>
-          
-          <code><a href="glog_opt_plot.html">glog_opt_plot()</a></code> 
+
+          <code><a href="glog_opt_plot.html">glog_opt_plot()</a></code>
+
         </dt>
         <dd>Glog optimisation</dd>
       </dl><dl><dt>
-          
-          <code><a href="glog_transform.html">glog_transform()</a></code> 
+
+          <code><a href="glog_transform.html">glog_transform()</a></code>
+
         </dt>
         <dd>Generalised logarithmic transform</dd>
       </dl><dl><dt>
-          
-          <code><a href="grid_search_1d.html">grid_search_1d()</a></code> 
+
+          <code><a href="grid_search_1d.html">grid_search_1d()</a></code>
+
         </dt>
         <dd>One dimensional grid search</dd>
       </dl><dl><dt>
-          
-          <code><a href="gs_line.html">gs_line()</a></code> 
+
+          <code><a href="gs_line.html">gs_line()</a></code>
+
         </dt>
         <dd>Grid search line plot</dd>
       </dl><dl><dt>
-          
-          <code><a href="hca_dendrogram.html">hca_dendrogram()</a></code> 
+
+          <code><a href="hca_dendrogram.html">hca_dendrogram()</a></code>
+
         </dt>
         <dd>HCA dendrogram</dd>
       </dl><dl><dt>
-          
-          <code><a href="kfold_xval.html">kfold_xval()</a></code> 
+
+          <code><a href="kfold_xval.html">kfold_xval()</a></code>
+
         </dt>
         <dd>k-fold cross-validation</dd>
       </dl><dl><dt>
-          
-          <code><a href="kfoldxcv_grid.html">kfoldxcv_grid()</a></code> 
+
+          <code><a href="kfoldxcv_grid.html">kfoldxcv_grid()</a></code>
+
         </dt>
         <dd>k-fold cross validation plot</dd>
       </dl><dl><dt>
-          
-          <code><a href="kfoldxcv_metric.html">kfoldxcv_metric()</a></code> 
+
+          <code><a href="kfoldxcv_metric.html">kfoldxcv_metric()</a></code>
+
         </dt>
         <dd>kfoldxcv metric plot</dd>
       </dl><dl><dt>
-          
-          <code><a href="knn_impute.html">knn_impute()</a></code> 
+
+          <code><a href="knn_impute.html">knn_impute()</a></code>
+
         </dt>
         <dd>kNN missing value imputation</dd>
       </dl><dl><dt>
-          
-          <code><a href="kw_p_hist.html">kw_p_hist()</a></code> 
+
+          <code><a href="kw_p_hist.html">kw_p_hist()</a></code>
+
         </dt>
         <dd>Histogram of p values</dd>
       </dl><dl><dt>
-          
-          <code><a href="kw_rank_sum.html">kw_rank_sum()</a></code> 
+
+          <code><a href="kw_rank_sum.html">kw_rank_sum()</a></code>
+
         </dt>
         <dd>Kruskal-Wallis rank sum test</dd>
       </dl><dl><dt>
-          
-          <code><a href="linear_model.html">linear_model()</a></code> 
+
+          <code><a href="linear_model.html">linear_model()</a></code>
+
         </dt>
         <dd>Linear model</dd>
       </dl><dl><dt>
-          
-          <code><a href="log_transform.html">log_transform()</a></code> 
+
+          <code><a href="log_transform.html">log_transform()</a></code>
+
         </dt>
         <dd>logarithm transform</dd>
       </dl><dl><dt>
-          
-          <code><a href="mean_centre.html">mean_centre()</a></code> 
+
+          <code><a href="mean_centre.html">mean_centre()</a></code>
+
         </dt>
         <dd>Mean centre</dd>
       </dl><dl><dt>
-          
-          <code><a href="mean_of_medians.html">mean_of_medians()</a></code> 
+
+          <code><a href="mean_of_medians.html">mean_of_medians()</a></code>
+
         </dt>
         <dd>Mean of medians</dd>
       </dl><dl><dt>
-          
-          <code><a href="mixed_effect.html">mixed_effect()</a></code> 
+
+          <code><a href="mixed_effect.html">mixed_effect()</a></code>
+
         </dt>
         <dd>Mixed effects model</dd>
       </dl><dl><dt>
-          
-          <code><a href="model_apply.html">model_apply(<i>&lt;ANOVA&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;HSD&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;mixed_effect&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;HSDEM&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;classical_lsq&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;confounders_clsq&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;constant_sum_norm&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;corr_coef&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;split_data&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;equal_split&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;filter_smeta&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;fisher_exact&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;fold_change&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;fold_change_int&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;HCA&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;knn_impute&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;kw_rank_sum&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;log_transform&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;mean_of_medians&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;nroot_transform&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;pairs_filter&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;prop_na&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;rsd_filter&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;sb_corr&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;stratified_split&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;tSNE&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;ttest&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;vec_norm&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;wilcox_test&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> 
+
+          <code><a href="model_apply.html">model_apply(<i>&lt;ANOVA&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;HSD&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;mixed_effect&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;HSDEM&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;classical_lsq&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;confounders_clsq&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;constant_sum_norm&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;corr_coef&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;split_data&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;equal_split&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;filter_smeta&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;fisher_exact&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;fold_change&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;fold_change_int&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;HCA&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;knn_impute&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;kw_rank_sum&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;log_transform&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;mean_of_medians&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;nroot_transform&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;pairs_filter&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;prop_na&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;rsd_filter&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;sb_corr&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;stratified_split&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;tSNE&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;ttest&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;vec_norm&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_apply.html">model_apply(<i>&lt;wilcox_test&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code>
+
         </dt>
         <dd>Apply method</dd>
       </dl><dl><dt>
-          
-          <code><a href="model_predict.html">model_predict(<i>&lt;DFA&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_predict.html">model_predict(<i>&lt;PCA&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_predict.html">model_predict(<i>&lt;PLSR&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_predict.html">model_predict(<i>&lt;PLSDA&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_predict.html">model_predict(<i>&lt;autoscale&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_predict.html">model_predict(<i>&lt;blank_filter&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_predict.html">model_predict(<i>&lt;constant_sum_norm&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_predict.html">model_predict(<i>&lt;dratio_filter&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_predict.html">model_predict(<i>&lt;filter_by_name&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_predict.html">model_predict(<i>&lt;filter_na_count&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_predict.html">model_predict(<i>&lt;filter_smeta&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_predict.html">model_predict(<i>&lt;glog_transform&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_predict.html">model_predict(<i>&lt;linear_model&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_predict.html">model_predict(<i>&lt;mean_centre&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_predict.html">model_predict(<i>&lt;mv_feature_filter&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_predict.html">model_predict(<i>&lt;mv_sample_filter&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_predict.html">model_predict(<i>&lt;OPLSR&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_predict.html">model_predict(<i>&lt;OPLSDA&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_predict.html">model_predict(<i>&lt;pareto_scale&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_predict.html">model_predict(<i>&lt;pqn_norm&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_predict.html">model_predict(<i>&lt;SVM&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_predict.html">model_predict(<i>&lt;vec_norm&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> 
+
+          <code><a href="model_predict.html">model_predict(<i>&lt;DFA&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_predict.html">model_predict(<i>&lt;PCA&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_predict.html">model_predict(<i>&lt;PLSR&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_predict.html">model_predict(<i>&lt;PLSDA&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_predict.html">model_predict(<i>&lt;autoscale&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_predict.html">model_predict(<i>&lt;blank_filter&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_predict.html">model_predict(<i>&lt;constant_sum_norm&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_predict.html">model_predict(<i>&lt;dratio_filter&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_predict.html">model_predict(<i>&lt;filter_by_name&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_predict.html">model_predict(<i>&lt;filter_na_count&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_predict.html">model_predict(<i>&lt;filter_smeta&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_predict.html">model_predict(<i>&lt;glog_transform&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_predict.html">model_predict(<i>&lt;linear_model&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_predict.html">model_predict(<i>&lt;mean_centre&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_predict.html">model_predict(<i>&lt;mv_feature_filter&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_predict.html">model_predict(<i>&lt;mv_sample_filter&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_predict.html">model_predict(<i>&lt;OPLSR&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_predict.html">model_predict(<i>&lt;OPLSDA&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_predict.html">model_predict(<i>&lt;pareto_scale&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_predict.html">model_predict(<i>&lt;pqn_norm&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_predict.html">model_predict(<i>&lt;SVM&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_predict.html">model_predict(<i>&lt;vec_norm&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code>
+
         </dt>
         <dd>Model prediction</dd>
       </dl><dl><dt>
-          
-          <code><a href="model_reverse.html">model_reverse(<i>&lt;autoscale&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_reverse.html">model_reverse(<i>&lt;mean_centre&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> 
+
+          <code><a href="model_reverse.html">model_reverse(<i>&lt;autoscale&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_reverse.html">model_reverse(<i>&lt;mean_centre&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code>
+
         </dt>
         <dd>Reverse preprocessing</dd>
       </dl><dl><dt>
-          
-          <code><a href="model_train.html">model_train(<i>&lt;DFA&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_train.html">model_train(<i>&lt;PCA&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_train.html">model_train(<i>&lt;PLSR&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_train.html">model_train(<i>&lt;PLSDA&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_train.html">model_train(<i>&lt;autoscale&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_train.html">model_train(<i>&lt;blank_filter&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_train.html">model_train(<i>&lt;constant_sum_norm&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_train.html">model_train(<i>&lt;dratio_filter&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_train.html">model_train(<i>&lt;filter_by_name&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_train.html">model_train(<i>&lt;filter_na_count&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_train.html">model_train(<i>&lt;filter_smeta&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_train.html">model_train(<i>&lt;glog_transform&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_train.html">model_train(<i>&lt;linear_model&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_train.html">model_train(<i>&lt;mean_centre&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_train.html">model_train(<i>&lt;mv_feature_filter&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_train.html">model_train(<i>&lt;mv_sample_filter&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_train.html">model_train(<i>&lt;OPLSR&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_train.html">model_train(<i>&lt;OPLSDA&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_train.html">model_train(<i>&lt;pareto_scale&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_train.html">model_train(<i>&lt;pqn_norm&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_train.html">model_train(<i>&lt;SVM&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_train.html">model_train(<i>&lt;vec_norm&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> 
+
+          <code><a href="model_train.html">model_train(<i>&lt;DFA&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_train.html">model_train(<i>&lt;PCA&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_train.html">model_train(<i>&lt;PLSR&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_train.html">model_train(<i>&lt;PLSDA&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_train.html">model_train(<i>&lt;autoscale&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_train.html">model_train(<i>&lt;blank_filter&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_train.html">model_train(<i>&lt;constant_sum_norm&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_train.html">model_train(<i>&lt;dratio_filter&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_train.html">model_train(<i>&lt;filter_by_name&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_train.html">model_train(<i>&lt;filter_na_count&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_train.html">model_train(<i>&lt;filter_smeta&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_train.html">model_train(<i>&lt;glog_transform&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_train.html">model_train(<i>&lt;linear_model&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_train.html">model_train(<i>&lt;mean_centre&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_train.html">model_train(<i>&lt;mv_feature_filter&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_train.html">model_train(<i>&lt;mv_sample_filter&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_train.html">model_train(<i>&lt;OPLSR&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_train.html">model_train(<i>&lt;OPLSDA&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_train.html">model_train(<i>&lt;pareto_scale&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_train.html">model_train(<i>&lt;pqn_norm&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_train.html">model_train(<i>&lt;SVM&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code> <code><a href="model_train.html">model_train(<i>&lt;vec_norm&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>)</a></code>
+
         </dt>
         <dd>Train a model</dd>
       </dl><dl><dt>
-          
-          <code><a href="mv_boxplot.html">mv_boxplot()</a></code> 
+
+          <code><a href="mv_boxplot.html">mv_boxplot()</a></code>
+
         </dt>
         <dd>Missing value boxplots</dd>
       </dl><dl><dt>
-          
-          <code><a href="mv_feature_filter.html">mv_feature_filter()</a></code> 
+
+          <code><a href="mv_feature_filter.html">mv_feature_filter()</a></code>
+
         </dt>
         <dd>Filter features by missing values</dd>
       </dl><dl><dt>
-          
-          <code><a href="mv_feature_filter_hist.html">mv_feature_filter_hist()</a></code> 
+
+          <code><a href="mv_feature_filter_hist.html">mv_feature_filter_hist()</a></code>
+
         </dt>
         <dd>Histogram of missing values per feature</dd>
       </dl><dl><dt>
-          
-          <code><a href="mv_histogram.html">mv_histogram()</a></code> 
+
+          <code><a href="mv_histogram.html">mv_histogram()</a></code>
+
         </dt>
         <dd>Missing value histogram</dd>
       </dl><dl><dt>
-          
-          <code><a href="mv_sample_filter.html">mv_sample_filter()</a></code> 
+
+          <code><a href="mv_sample_filter.html">mv_sample_filter()</a></code>
+
         </dt>
         <dd>Missing value sample filter</dd>
       </dl><dl><dt>
-          
-          <code><a href="mv_sample_filter_hist.html">mv_sample_filter_hist()</a></code> 
+
+          <code><a href="mv_sample_filter_hist.html">mv_sample_filter_hist()</a></code>
+
         </dt>
         <dd>Histogram of missing values per sample</dd>
       </dl><dl><dt>
-          
-          <code><a href="nroot_transform.html">nroot_transform()</a></code> 
+
+          <code><a href="nroot_transform.html">nroot_transform()</a></code>
+
         </dt>
         <dd>nth root transform</dd>
       </dl><dl><dt>
-          
-          <code><a href="ontology_cache.html">ontology_cache()</a></code> 
+
+          <code><a href="ontology_cache.html">ontology_cache()</a></code>
+
         </dt>
         <dd>ontology cache</dd>
       </dl><dl><dt>
-          
-          <code><a href="pairs_filter.html">pairs_filter()</a></code> 
+
+          <code><a href="pairs_filter.html">pairs_filter()</a></code>
+
         </dt>
         <dd>Pairs filter</dd>
       </dl><dl><dt>
-          
-          <code><a href="pareto_scale.html">pareto_scale()</a></code> 
+
+          <code><a href="pareto_scale.html">pareto_scale()</a></code>
+
         </dt>
         <dd>Pareto scaling</dd>
       </dl><dl><dt>
-          
-          <code><a href="pca_biplot.html">pca_biplot()</a></code> 
+
+          <code><a href="pca_biplot.html">pca_biplot()</a></code>
+
         </dt>
         <dd>PCA biplot</dd>
       </dl><dl><dt>
-          
-          <code><a href="pca_correlation_plot.html">pca_correlation_plot()</a></code> 
+
+          <code><a href="pca_correlation_plot.html">pca_correlation_plot()</a></code>
+
         </dt>
         <dd>PCA correlation plot</dd>
       </dl><dl><dt>
-          
-          <code><a href="pca_dstat_plot.html">pca_dstat_plot()</a></code> 
+
+          <code><a href="pca_dstat_plot.html">pca_dstat_plot()</a></code>
+
         </dt>
         <dd>d-statistic plot</dd>
       </dl><dl><dt>
-          
-          <code><a href="pca_loadings_plot.html">pca_loadings_plot()</a></code> 
+
+          <code><a href="pca_loadings_plot.html">pca_loadings_plot()</a></code>
+
         </dt>
         <dd>PCA loadings plot</dd>
       </dl><dl><dt>
-          
-          <code><a href="pca_scores_plot.html">pca_scores_plot()</a></code> 
+
+          <code><a href="pca_scores_plot.html">pca_scores_plot()</a></code>
+
         </dt>
         <dd>PCA scores plot</dd>
       </dl><dl><dt>
-          
-          <code><a href="pca_scree_plot.html">pca_scree_plot()</a></code> 
+
+          <code><a href="pca_scree_plot.html">pca_scree_plot()</a></code>
+
         </dt>
         <dd>Scree plot</dd>
       </dl><dl><dt>
-          
-          <code><a href="permutation_test.html">permutation_test()</a></code> 
+
+          <code><a href="permutation_test.html">permutation_test()</a></code>
+
         </dt>
         <dd>Permutation test</dd>
       </dl><dl><dt>
-          
-          <code><a href="permutation_test_plot.html">permutation_test_plot()</a></code> 
+
+          <code><a href="permutation_test_plot.html">permutation_test_plot()</a></code>
+
         </dt>
         <dd>permutation_test_plot class</dd>
       </dl><dl><dt>
-          
-          <code><a href="permute_sample_order.html">permute_sample_order()</a></code> 
+
+          <code><a href="permute_sample_order.html">permute_sample_order()</a></code>
+
         </dt>
         <dd>Permute Sample Order</dd>
       </dl><dl><dt>
-          
-          <code><a href="pls_regcoeff_plot.html">pls_regcoeff_plot()</a></code> 
+
+          <code><a href="pls_regcoeff_plot.html">pls_regcoeff_plot()</a></code>
+
         </dt>
         <dd>pls_regcoeff_plot class</dd>
       </dl><dl><dt>
-          
-          <code><a href="pls_scores_plot.html">pls_scores_plot()</a></code> <code><a href="pls_scores_plot.html">plsda_scores_plot()</a></code> 
+
+          <code><a href="pls_scores_plot.html">pls_scores_plot()</a></code> <code><a href="pls_scores_plot.html">plsda_scores_plot()</a></code>
+
         </dt>
         <dd>PLSDA scores plot</dd>
       </dl><dl><dt>
-          
-          <code><a href="pls_vip_plot.html">pls_vip_plot()</a></code> 
+
+          <code><a href="pls_vip_plot.html">pls_vip_plot()</a></code>
+
         </dt>
         <dd>PLSDA VIP plot</dd>
       </dl><dl><dt>
-          
-          <code><a href="plsda_feature_importance_plot.html">plsda_feature_importance_plot()</a></code> 
+
+          <code><a href="plsda_feature_importance_plot.html">plsda_feature_importance_plot()</a></code>
+
         </dt>
         <dd>PLSDA feature importance summary plot</dd>
       </dl><dl><dt>
-          
-          <code><a href="plsda_predicted_plot.html">plsda_predicted_plot()</a></code> 
+
+          <code><a href="plsda_predicted_plot.html">plsda_predicted_plot()</a></code>
+
         </dt>
         <dd>PLSDA predicted plot</dd>
       </dl><dl><dt>
-          
-          <code><a href="plsda_roc_plot.html">plsda_roc_plot()</a></code> 
+
+          <code><a href="plsda_roc_plot.html">plsda_roc_plot()</a></code>
+
         </dt>
         <dd>PLSDA ROC plot</dd>
       </dl><dl><dt>
-          
-          <code><a href="plsr_cook_dist.html">plsr_cook_dist()</a></code> 
+
+          <code><a href="plsr_cook_dist.html">plsr_cook_dist()</a></code>
+
         </dt>
         <dd>Cook's distance barchart</dd>
       </dl><dl><dt>
-          
-          <code><a href="plsr_prediction_plot.html">plsr_prediction_plot()</a></code> 
+
+          <code><a href="plsr_prediction_plot.html">plsr_prediction_plot()</a></code>
+
         </dt>
         <dd>PLSR prediction plot</dd>
       </dl><dl><dt>
-          
-          <code><a href="plsr_qq_plot.html">plsr_qq_plot()</a></code> 
+
+          <code><a href="plsr_qq_plot.html">plsr_qq_plot()</a></code>
+
         </dt>
         <dd>PLSR QQ plot</dd>
       </dl><dl><dt>
-          
-          <code><a href="plsr_residual_hist.html">plsr_residual_hist()</a></code> 
+
+          <code><a href="plsr_residual_hist.html">plsr_residual_hist()</a></code>
+
         </dt>
         <dd>PLSR residuals histogram</dd>
       </dl><dl><dt>
-          
-          <code><a href="pqn_norm.html">pqn_norm()</a></code> 
+
+          <code><a href="pqn_norm.html">pqn_norm()</a></code>
+
         </dt>
         <dd>Probabilistic Quotient Normalisation (PQN)</dd>
       </dl><dl><dt>
-          
-          <code><a href="pqn_norm_hist.html">pqn_norm_hist()</a></code> 
+
+          <code><a href="pqn_norm_hist.html">pqn_norm_hist()</a></code>
+
         </dt>
         <dd>PQN coefficient histogram</dd>
       </dl><dl><dt>
-          
-          <code><a href="prop_na.html">prop_na()</a></code> 
+
+          <code><a href="prop_na.html">prop_na()</a></code>
+
         </dt>
         <dd>Fisher's exact test for missing values</dd>
       </dl><dl><dt>
-          
-          <code><a href="r_squared.html">r_squared()</a></code> 
+
+          <code><a href="r_squared.html">r_squared()</a></code>
+
         </dt>
         <dd>Coefficient of determination (R-squared)</dd>
       </dl><dl><dt>
-          
-          <code><a href="resample.html">resample()</a></code> 
+
+          <code><a href="resample.html">resample()</a></code>
+
         </dt>
         <dd>Data resampling</dd>
       </dl><dl><dt>
-          
-          <code><a href="resample_chart.html">resample_chart()</a></code> 
+
+          <code><a href="resample_chart.html">resample_chart()</a></code>
+
         </dt>
         <dd>resample_chart class</dd>
       </dl><dl><dt>
-          
-          <code><a href="rsd_filter.html">rsd_filter()</a></code> 
+
+          <code><a href="rsd_filter.html">rsd_filter()</a></code>
+
         </dt>
         <dd>RSD filter</dd>
       </dl><dl><dt>
-          
-          <code><a href="rsd_filter_hist.html">rsd_filter_hist()</a></code> 
+
+          <code><a href="rsd_filter_hist.html">rsd_filter_hist()</a></code>
+
         </dt>
         <dd>RSD histogram</dd>
       </dl><dl><dt>
-          
-          <code><a href="run.html">run(<i>&lt;bootstrap&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>,<i>&lt;metric&gt;</i>)</a></code> <code><a href="run.html">run(<i>&lt;forward_selection_by_rank&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>,<i>&lt;metric&gt;</i>)</a></code> <code><a href="run.html">run(<i>&lt;grid_search_1d&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>,<i>&lt;metric&gt;</i>)</a></code> <code><a href="run.html">run(<i>&lt;kfold_xval&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>,<i>&lt;metric&gt;</i>)</a></code> <code><a href="run.html">run(<i>&lt;permutation_test&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>,<i>&lt;metric&gt;</i>)</a></code> <code><a href="run.html">run(<i>&lt;permute_sample_order&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>,<i>&lt;metric&gt;</i>)</a></code> <code><a href="run.html">run(<i>&lt;resample&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>,<i>&lt;metric&gt;</i>)</a></code> 
+
+          <code><a href="run.html">run(<i>&lt;bootstrap&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>,<i>&lt;metric&gt;</i>)</a></code> <code><a href="run.html">run(<i>&lt;forward_selection_by_rank&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>,<i>&lt;metric&gt;</i>)</a></code> <code><a href="run.html">run(<i>&lt;grid_search_1d&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>,<i>&lt;metric&gt;</i>)</a></code> <code><a href="run.html">run(<i>&lt;kfold_xval&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>,<i>&lt;metric&gt;</i>)</a></code> <code><a href="run.html">run(<i>&lt;permutation_test&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>,<i>&lt;metric&gt;</i>)</a></code> <code><a href="run.html">run(<i>&lt;permute_sample_order&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>,<i>&lt;metric&gt;</i>)</a></code> <code><a href="run.html">run(<i>&lt;resample&gt;</i>,<i>&lt;DatasetExperiment&gt;</i>,<i>&lt;metric&gt;</i>)</a></code>
+
         </dt>
         <dd>Runs an iterator, applying the chosen model multiple times.</dd>
       </dl><dl><dt>
-          
-          <code><a href="sb_corr.html">sb_corr()</a></code> 
+
+          <code><a href="sb_corr.html">sb_corr()</a></code>
+
         </dt>
         <dd>Signal/batch correction for mass spectrometry data</dd>
       </dl><dl><dt>
-          
-          <code><a href="scatter_chart.html">scatter_chart()</a></code> 
+
+          <code><a href="scatter_chart.html">scatter_chart()</a></code>
+
         </dt>
         <dd>Group scatter chart</dd>
       </dl><dl><dt>
-          
-          <code><a href="split_data.html">split_data()</a></code> 
+
+          <code><a href="split_data.html">split_data()</a></code>
+
         </dt>
         <dd>Split data</dd>
       </dl><dl><dt>
-          
-          <code><a href="stratified_split.html">stratified_split()</a></code> 
+
+          <code><a href="stratified_split.html">stratified_split()</a></code>
+
         </dt>
         <dd>Stratified sampling</dd>
       </dl><dl><dt>
-          
-          <code><a href="structToolbox.html">structToolbox</a></code> 
-        </dt>
-        <dd>structToolbox: Examples of tools built using the Statistics in R Using Class Templates (struct) package</dd>
-      </dl><dl><dt>
-          
-          <code><a href="svm_plot_2d.html">svm_plot_2d()</a></code> 
+
+          <code><a href="svm_plot_2d.html">svm_plot_2d()</a></code>
+
         </dt>
         <dd>SVM scatter plot</dd>
       </dl><dl><dt>
-          
-          <code><a href="tSNE.html">tSNE()</a></code> 
+
+          <code><a href="tSNE.html">tSNE()</a></code>
+
         </dt>
         <dd>tSNE</dd>
       </dl><dl><dt>
-          
-          <code><a href="tSNE_scatter.html">tSNE_scatter()</a></code> 
+
+          <code><a href="tSNE_scatter.html">tSNE_scatter()</a></code>
+
         </dt>
         <dd>Feature boxplot</dd>
       </dl><dl><dt>
-          
-          <code><a href="tic_chart.html">tic_chart()</a></code> 
+
+          <code><a href="tic_chart.html">tic_chart()</a></code>
+
         </dt>
         <dd>Total Ion Count chart.</dd>
       </dl><dl><dt>
-          
-          <code><a href="ttest.html">ttest()</a></code> 
+
+          <code><a href="ttest.html">ttest()</a></code>
+
         </dt>
         <dd>t-test</dd>
       </dl><dl><dt>
-          
-          <code><a href="vec_norm.html">vec_norm()</a></code> 
+
+          <code><a href="vec_norm.html">vec_norm()</a></code>
+
         </dt>
         <dd>Vector normalisation</dd>
       </dl><dl><dt>
-          
-          <code><a href="wilcox_p_hist.html">wilcox_p_hist()</a></code> 
+
+          <code><a href="wilcox_p_hist.html">wilcox_p_hist()</a></code>
+
         </dt>
         <dd>Histogram of p values</dd>
       </dl><dl><dt>
-          
-          <code><a href="wilcox_test.html">wilcox_test()</a></code> 
+
+          <code><a href="wilcox_test.html">wilcox_test()</a></code>
+
         </dt>
         <dd>wilcoxon signed rank test</dd>
       </dl></div>
@@ -652,18 +752,18 @@ <h2 id="all-functions">All functions<a class="anchor" aria-label="anchor" href="
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
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diff --git a/reference/kfold_xval.html b/reference/kfold_xval.html
index 21bc222..ce0f6d0 100644
--- a/reference/kfold_xval.html
+++ b/reference/kfold_xval.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content='k-fold cross-validation is an iterative approach applied to validate models. The samples are divided into k "folds", or subsets. Each subset is excluded from model training and used for model validation once, resulting in a single left-out prediction for each sample. Model performance metrics are then computed for the training and test sets across all folds.'><title>k-fold cross-validation — kfold_xval • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="k-fold cross-validation — kfold_xval"><meta property="og:description" content='k-fold cross-validation is an iterative approach applied to validate models. The samples are divided into k "folds", or subsets. Each subset is excluded from model training and used for model validation once, resulting in a single left-out prediction for each sample. Model performance metrics are then computed for the training and test sets across all folds.'><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>k-fold cross-validation — kfold_xval • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="k-fold cross-validation — kfold_xval"><meta name="description" content='k-fold cross-validation is an iterative approach applied to validate models. The samples are divided into k "folds", or subsets. Each subset is excluded from model training and used for model validation once, resulting in a single left-out prediction for each sample. Model performance metrics are then computed for the training and test sets across all folds.'><meta property="og:description" content='k-fold cross-validation is an iterative approach applied to validate models. The samples are divided into k "folds", or subsets. Each subset is excluded from model training and used for model validation once, resulting in a single left-out prediction for each sample. Model performance metrics are then computed for the training and test sets across all folds.'></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary"><div class="container">
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     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.17.3</small>
+
 
-    
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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-      <ul class="navbar-nav me-auto"><li class="active nav-item">
-  <a class="nav-link" href="../reference/index.html">Reference</a>
-</li>
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>k-fold cross-validation</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/kfold_xval_class.R" class="external-link"><code>R/kfold_xval_class.R</code></a></small>
+
+      <h1>k-fold cross-validation</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/kfold_xval_class.R" class="external-link"><code>R/kfold_xval_class.R</code></a></small>
       <div class="d-none name"><code>kfold_xval.Rd</code></div>
     </div>
 
@@ -59,37 +47,37 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>folds</dt>
+
+
+<dl><dt id="arg-folds">folds<a class="anchor" aria-label="anchor" href="#arg-folds"></a></dt>
 <dd><p>(numeric, integer) The number of cross-validation folds. The default is <code>10</code>.<br></p></dd>
 
 
-<dt>method</dt>
+<dt id="arg-method">method<a class="anchor" aria-label="anchor" href="#arg-method"></a></dt>
 <dd><p>(character) Fold selection method. Allowed values are limited to the following:</p><ul><li><p><code>"venetian"</code>: Every nth sample is assigned to the same fold, where n is the number of folds.</p></li>
 <li><p><code>"blocks"</code>: Blocks of adjacent samples are assigned to the same fold.</p></li>
 <li><p><code>"random"</code>: Samples are randomly assigned to a fold.</p></li>
 </ul><p>The default is <code>"venetian"</code>.</p></dd>
 
 
-<dt>factor_name</dt>
+<dt id="arg-factor-name">factor_name<a class="anchor" aria-label="anchor" href="#arg-factor-name"></a></dt>
 <dd><p>(character) The name of a sample-meta column to use.</p></dd>
 
 
-<dt>collect</dt>
+<dt id="arg-collect">collect<a class="anchor" aria-label="anchor" href="#arg-collect"></a></dt>
 <dd><p>(NULL, character) The name of a model output to collect over all bootstrap repetitions, in addition to the input metric. The default is <code>NULL</code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>kfold_xval</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>results</code></td><td>(data.frame)</td></tr><tr><td><code>metric</code></td><td>(data.frame)</td></tr><tr><td><code>metric.train</code></td><td>(numeric)</td></tr><tr><td><code>metric.test</code></td><td>(numeric)</td></tr><tr><td><code>collected</code></td><td>(list)</td></tr></table></div>
+    <p>A  <code>kfold_xval</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>results</code></td><td>(data.frame)</td></tr><tr><td><code>metric</code></td><td>(data.frame)</td></tr><tr><td><code>metric.train</code></td><td>(numeric)</td></tr><tr><td><code>metric.test</code></td><td>(numeric)</td></tr><tr><td><code>collected</code></td><td>(list)</td></tr></table></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>kfold_xval</code> object inherits the following <code>struct</code> classes: <br><br><code>[kfold_xval]</code> &gt;&gt; <code>[resampler]</code> &gt;&gt; <code>[iterator]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -109,23 +97,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/kfoldxcv_grid-1.png b/reference/kfoldxcv_grid-1.png
index 21f3ca7..34b7332 100644
Binary files a/reference/kfoldxcv_grid-1.png and b/reference/kfoldxcv_grid-1.png differ
diff --git a/reference/kfoldxcv_grid.html b/reference/kfoldxcv_grid.html
index 8331dac..32f31d2 100644
--- a/reference/kfoldxcv_grid.html
+++ b/reference/kfoldxcv_grid.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="A graphic for visualising the true class and the predicted class of samples in all groups for all cross-validation folds."><title>k-fold cross validation plot — kfoldxcv_grid • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="k-fold cross validation plot — kfoldxcv_grid"><meta property="og:description" content="A graphic for visualising the true class and the predicted class of samples in all groups for all cross-validation folds."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>k-fold cross validation plot — kfoldxcv_grid • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="k-fold cross validation plot — kfoldxcv_grid"><meta name="description" content="A graphic for visualising the true class and the predicted class of samples in all groups for all cross-validation folds."><meta property="og:description" content="A graphic for visualising the true class and the predicted class of samples in all groups for all cross-validation folds."></head><body>
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-    
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary"><div class="container">
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+
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.17.3</small>
+
 
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-      <ul class="navbar-nav me-auto"><li class="active nav-item">
-  <a class="nav-link" href="../reference/index.html">Reference</a>
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>k-fold cross validation plot</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/kfold_xval_charts.R" class="external-link"><code>R/kfold_xval_charts.R</code></a></small>
+
+      <h1>k-fold cross validation plot</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/kfold_xval_charts.R" class="external-link"><code>R/kfold_xval_charts.R</code></a></small>
       <div class="d-none name"><code>kfoldxcv_grid.Rd</code></div>
     </div>
 
@@ -59,30 +47,30 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>factor_name</dt>
+
+
+<dl><dt id="arg-factor-name">factor_name<a class="anchor" aria-label="anchor" href="#arg-factor-name"></a></dt>
 <dd><p>(character) The name of a sample-meta column to use.</p></dd>
 
 
-<dt>level</dt>
+<dt id="arg-level">level<a class="anchor" aria-label="anchor" href="#arg-level"></a></dt>
 <dd><p>(character) The level/group to plot.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>
+    <p>A  <code>
 kfoldxcv_grid
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>kfoldxcv_grid</code> object inherits the following <code>struct</code> classes: <br><br><code>[kfoldxcv_grid]</code> &gt;&gt; <code>[chart]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -100,28 +88,29 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">kfoldxcv_grid</span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'Species'</span>,level<span class="op">=</span><span class="st">'setosa'</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="chart_plot.html">chart_plot</a></span><span class="op">(</span><span class="va">C</span>,<span class="va">I</span><span class="op">)</span></span></span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Removed 900 rows containing missing values (`geom_tile()`).</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Removed 900 rows containing missing values or values outside the scale range</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> (`geom_tile()`).</span>
 <span class="r-plt img"><img src="kfoldxcv_grid-1.png" alt="" width="700" height="433"></span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/kfoldxcv_metric.html b/reference/kfoldxcv_metric.html
index f958f09..4206b13 100644
--- a/reference/kfoldxcv_metric.html
+++ b/reference/kfoldxcv_metric.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>kfoldxcv metric plot — kfoldxcv_metric • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="kfoldxcv metric plot — kfoldxcv_metric"><meta name="description" content="A boxplot of the performance metric computed for each fold of a k-fold cross-validation."><meta property="og:description" content="A boxplot of the performance metric computed for each fold of a k-fold cross-validation."></head><body>
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     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
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 <li class="nav-item dropdown">
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+  </ul></li>
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+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>kfoldxcv metric plot</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/kfold_xval_charts.R" class="external-link"><code>R/kfold_xval_charts.R</code></a></small>
+
+      <h1>kfoldxcv metric plot</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/kfold_xval_charts.R" class="external-link"><code>R/kfold_xval_charts.R</code></a></small>
       <div class="d-none name"><code>kfoldxcv_metric.Rd</code></div>
     </div>
 
@@ -59,22 +47,22 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>...</dt>
+
+
+<dl><dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>
+    <p>A  <code>
 kfoldxcv_metric
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>kfoldxcv_metric</code> object inherits the following <code>struct</code> classes: <br><br><code>[kfoldxcv_metric]</code> &gt;&gt; <code>[chart]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -86,23 +74,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">kfoldxcv_metric</span><span class="op">(</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
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-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
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diff --git a/reference/knn_impute.html b/reference/knn_impute.html
index ff19afe..708b90c 100644
--- a/reference/knn_impute.html
+++ b/reference/knn_impute.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="k-nearest neighbour missing value imputation replaces missing values in the data with the average of a predefined number of the most similar neighbours for which the value is present"><title>kNN missing value imputation — knn_impute • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="kNN missing value imputation — knn_impute"><meta property="og:description" content="k-nearest neighbour missing value imputation replaces missing values in the data with the average of a predefined number of the most similar neighbours for which the value is present"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>kNN missing value imputation — knn_impute • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="kNN missing value imputation — knn_impute"><meta name="description" content="k-nearest neighbour missing value imputation replaces missing values in the data with the average of a predefined number of the most similar neighbours for which the value is present"><meta property="og:description" content="k-nearest neighbour missing value imputation replaces missing values in the data with the average of a predefined number of the most similar neighbours for which the value is present"></head><body>
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     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.17.3</small>
+
 
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>kNN missing value imputation</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/knn_impute_class.R" class="external-link"><code>R/knn_impute_class.R</code></a></small>
+
+      <h1>kNN missing value imputation</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/knn_impute_class.R" class="external-link"><code>R/knn_impute_class.R</code></a></small>
       <div class="d-none name"><code>knn_impute.Rd</code></div>
     </div>
 
@@ -65,47 +53,46 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>neighbours</dt>
+
+
+<dl><dt id="arg-neighbours">neighbours<a class="anchor" aria-label="anchor" href="#arg-neighbours"></a></dt>
 <dd><p>(numeric) The number of neighbours (k) to use for imputation. The default is <code>5</code>.<br></p></dd>
 
 
-<dt>sample_max</dt>
+<dt id="arg-sample-max">sample_max<a class="anchor" aria-label="anchor" href="#arg-sample-max"></a></dt>
 <dd><p>(numeric) The maximum percent missing values per sample. The default is <code>50</code>.<br></p></dd>
 
 
-<dt>feature_max</dt>
+<dt id="arg-feature-max">feature_max<a class="anchor" aria-label="anchor" href="#arg-feature-max"></a></dt>
 <dd><p>(numeric) The maximum percent missing values per feature. The default is <code>50</code>.<br></p></dd>
 
 
-<dt>by</dt>
+<dt id="arg-by">by<a class="anchor" aria-label="anchor" href="#arg-by"></a></dt>
 <dd><p>(character) Impute using similar "samples" or "features". Default features. The default is <code>"features"</code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>knn_impute</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>imputed</code></td><td>(DatasetExperiment) A DatasetExperiment object containing the data where missing values have been imputed.</td></tr></table></div>
+    <p>A  <code>knn_impute</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>imputed</code></td><td>(DatasetExperiment) A DatasetExperiment object containing the data where missing values have been imputed.</td></tr></table></div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>pmp</code></p></li>
 </ul></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>knn_impute</code> object inherits the following <code>struct</code> classes: <br><br><code>[knn_impute]</code> &gt;&gt; <code>[model]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
     <div class="section level2">
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
-    <p>Jankevics A, Lloyd GR, Weber RJM (2023). <em>pmp: Peak Matrix Processing
-and signal batch correction for metabolomics datasets</em>.
-doi:10.18129/B9.bioc.pmp <a href="https://doi.org/10.18129/B9.bioc.pmp" class="external-link">https://doi.org/10.18129/B9.bioc.pmp</a>, R
-package version 1.14.0, <a href="https://bioconductor.org/packages/pmp" class="external-link">https://bioconductor.org/packages/pmp</a>.</p>
+    <p>Jankevics A, Lloyd GR, Weber RJM (????). <em>pmp: Peak Matrix Processing
+and signal batch correction for metabolomics datasets</em>. R package
+version 1.15.1.</p>
     </div>
 
     <div class="section level2">
@@ -119,23 +106,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">knn_impute</span><span class="op">(</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
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diff --git a/reference/kw_p_hist.html b/reference/kw_p_hist.html
index 35a8bfb..bc54e50 100644
--- a/reference/kw_p_hist.html
+++ b/reference/kw_p_hist.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="A histogram of the p-values computed by the kruskal-wallis method"><title>Histogram of p values — kw_p_hist • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Histogram of p values — kw_p_hist"><meta property="og:description" content="A histogram of the p-values computed by the kruskal-wallis method"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Histogram of p values — kw_p_hist • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Histogram of p values — kw_p_hist"><meta name="description" content="A histogram of the p-values computed by the kruskal-wallis method"><meta property="og:description" content="A histogram of the p-values computed by the kruskal-wallis method"></head><body>
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Histogram of p values</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/kw_rank_sum_class.R" class="external-link"><code>R/kw_rank_sum_class.R</code></a></small>
+
+      <h1>Histogram of p values</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/kw_rank_sum_class.R" class="external-link"><code>R/kw_rank_sum_class.R</code></a></small>
       <div class="d-none name"><code>kw_p_hist.Rd</code></div>
     </div>
 
@@ -59,22 +47,22 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>...</dt>
+
+
+<dl><dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>
+    <p>A  <code>
 kw_p_hist
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>kw_p_hist</code> object inherits the following <code>struct</code> classes: <br><br><code>[kw_p_hist]</code> &gt;&gt; <code>[chart]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -86,23 +74,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">kw_p_hist</span><span class="op">(</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/kw_rank_sum.html b/reference/kw_rank_sum.html
index d023a33..91f5e06 100644
--- a/reference/kw_rank_sum.html
+++ b/reference/kw_rank_sum.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="The Kruskal-Wallis test is a univariate hypothesis testing method that allows multiple (n&amp;gt;=2) groups to be compared without making the assumption that values are normally distributed. It is the non-parametric equivalent of a 1-way ANOVA. The test is applied to all variables/features individually, and multiple test corrected p-values are computed to indicate the significance of variables/features."><title>Kruskal-Wallis rank sum test — kw_rank_sum • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Kruskal-Wallis rank sum test — kw_rank_sum"><meta property="og:description" content="The Kruskal-Wallis test is a univariate hypothesis testing method that allows multiple (n&amp;gt;=2) groups to be compared without making the assumption that values are normally distributed. It is the non-parametric equivalent of a 1-way ANOVA. The test is applied to all variables/features individually, and multiple test corrected p-values are computed to indicate the significance of variables/features."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Kruskal-Wallis rank sum test — kw_rank_sum • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Kruskal-Wallis rank sum test — kw_rank_sum"><meta name="description" content="The Kruskal-Wallis test is a univariate hypothesis testing method that allows multiple (n&amp;gt;=2) groups to be compared without making the assumption that values are normally distributed. It is the non-parametric equivalent of a 1-way ANOVA. The test is applied to all variables/features individually, and multiple test corrected p-values are computed to indicate the significance of variables/features."><meta property="og:description" content="The Kruskal-Wallis test is a univariate hypothesis testing method that allows multiple (n&amp;gt;=2) groups to be compared without making the assumption that values are normally distributed. It is the non-parametric equivalent of a 1-way ANOVA. The test is applied to all variables/features individually, and multiple test corrected p-values are computed to indicate the significance of variables/features."></head><body>
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+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.17.3</small>
+
 
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
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+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
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-      <img src="" class="logo" alt=""><h1>Kruskal-Wallis rank sum test</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/kw_rank_sum_class.R" class="external-link"><code>R/kw_rank_sum_class.R</code></a></small>
+
+      <h1>Kruskal-Wallis rank sum test</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/kw_rank_sum_class.R" class="external-link"><code>R/kw_rank_sum_class.R</code></a></small>
       <div class="d-none name"><code>kw_rank_sum.Rd</code></div>
     </div>
 
@@ -59,33 +47,33 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>alpha</dt>
+
+
+<dl><dt id="arg-alpha">alpha<a class="anchor" aria-label="anchor" href="#arg-alpha"></a></dt>
 <dd><p>(numeric) The p-value cutoff for determining significance. The default is <code>0.05</code>.<br></p></dd>
 
 
-<dt>mtc</dt>
+<dt id="arg-mtc">mtc<a class="anchor" aria-label="anchor" href="#arg-mtc"></a></dt>
 <dd><p>(character) Multiple test correction method. Allowed values are limited to the following:</p><ul><li><p><code>"bonferroni"</code>: Bonferroni correction in which the p-values are multiplied by the number of comparisons.</p></li>
 <li><p><code>"fdr"</code>: Benjamini and Hochberg False Discovery Rate correction.</p></li>
 <li><p><code>"none"</code>: No correction.</p></li>
 </ul><p>The default is <code>"fdr"</code>.</p></dd>
 
 
-<dt>factor_names</dt>
+<dt id="arg-factor-names">factor_names<a class="anchor" aria-label="anchor" href="#arg-factor-names"></a></dt>
 <dd><p>(character) The name of sample meta column(s) to use.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>kw_rank_sum</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>test_statistic</code></td><td>(data.frame) The value of the calculated statistic which is converted to a p-value when compared to a chi2-distribution.</td></tr><tr><td><code>p_value</code></td><td>(data.frame) The probability of observing the calculated statistic.</td></tr><tr><td><code>dof</code></td><td>(numeric) The number of degrees of freedom used to calculate the test statistic.</td></tr><tr><td><code>significant</code></td><td>(data.frame) TRUE if the calculated p-value is less than the supplied threhold (alpha).</td></tr><tr><td><code>estimates</code></td><td>(data.frame)</td></tr></table></div>
+    <p>A  <code>kw_rank_sum</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>test_statistic</code></td><td>(data.frame) The value of the calculated statistic which is converted to a p-value when compared to a chi2-distribution.</td></tr><tr><td><code>p_value</code></td><td>(data.frame) The probability of observing the calculated statistic.</td></tr><tr><td><code>dof</code></td><td>(numeric) The number of degrees of freedom used to calculate the test statistic.</td></tr><tr><td><code>significant</code></td><td>(data.frame) TRUE if the calculated p-value is less than the supplied threhold (alpha).</td></tr><tr><td><code>estimates</code></td><td>(data.frame)</td></tr></table></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>kw_rank_sum</code> object inherits the following <code>struct</code> classes: <br><br><code>[kw_rank_sum]</code> &gt;&gt; <code>[model]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -102,23 +90,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="model_apply.html">model_apply</a></span><span class="op">(</span><span class="va">M</span>,<span class="va">D</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
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diff --git a/reference/linear_model.html b/reference/linear_model.html
index 94a08b3..7412bb8 100644
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@@ -1,50 +1,38 @@
 <!DOCTYPE html>
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-      <img src="" class="logo" alt=""><h1>Linear model</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/linear_model_class.R" class="external-link"><code>R/linear_model_class.R</code></a></small>
+
+      <h1>Linear model</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/linear_model_class.R" class="external-link"><code>R/linear_model_class.R</code></a></small>
       <div class="d-none name"><code>linear_model.Rd</code></div>
     </div>
 
@@ -59,11 +47,13 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>formula</dt>
+
+
+<dl><dt id="arg-formula">formula<a class="anchor" aria-label="anchor" href="#arg-formula"></a></dt>
 <dd><p>(formula) A symbolic description of the model to be fitted.</p></dd>
 
 
-<dt>na_action</dt>
+<dt id="arg-na-action">na_action<a class="anchor" aria-label="anchor" href="#arg-na-action"></a></dt>
 <dd><p>(character) NA action. Allowed values are limited to the following:</p><ul><li><p><code>"na.omit"</code>: Incomplete cases are removed.</p></li>
 <li><p><code>"na.fail"</code>: An error is thrown if NA are present.</p></li>
 <li><p><code>"na.exclude"</code>: Incomplete cases are removed, and the output result is padded to the correct size using NA.</p></li>
@@ -71,32 +61,30 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </ul><p>The default is <code>"na.omit"</code>.</p></dd>
 
 
-<dt>contrasts</dt>
+<dt id="arg-contrasts">contrasts<a class="anchor" aria-label="anchor" href="#arg-contrasts"></a></dt>
 <dd><p>(list) The contrasts associated with a factor. The default is <code><a href="https://rdrr.io/r/base/list.html" class="external-link">list()</a></code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>linear_model</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>lm</code></td><td>(lm) The lm object for this model_.</td></tr><tr><td><code>coefficients</code></td><td>(numeric) The coefficients for the fitted model_.</td></tr><tr><td><code>residuals</code></td><td>(numeric) The residuals for the fitted model_.</td></tr><tr><td><code>fitted_values</code></td><td>(numeric) The fitted values for the data used to train the model_.</td></tr><tr><td><code>predicted_values</code></td><td>(numeric) The predicted values for new data using the fitted model_.</td></tr><tr><td><code>r_squared</code></td><td>(numeric) The value of R Squared for the fitted model_.</td></tr><tr><td><code>adj_r_squared</code></td><td>(numeric) The value ofAdjusted  R Squared for the fitted model_.</td></tr></table></div>
+    <p>A  <code>linear_model</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>lm</code></td><td>(lm) The lm object for this model_.</td></tr><tr><td><code>coefficients</code></td><td>(numeric) The coefficients for the fitted model_.</td></tr><tr><td><code>residuals</code></td><td>(numeric) The residuals for the fitted model_.</td></tr><tr><td><code>fitted_values</code></td><td>(numeric) The fitted values for the data used to train the model_.</td></tr><tr><td><code>predicted_values</code></td><td>(numeric) The predicted values for new data using the fitted model_.</td></tr><tr><td><code>r_squared</code></td><td>(numeric) The value of R Squared for the fitted model_.</td></tr><tr><td><code>adj_r_squared</code></td><td>(numeric) The value ofAdjusted  R Squared for the fitted model_.</td></tr></table></div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>stats</code></p></li>
 </ul></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>linear_model</code> object inherits the following <code>struct</code> classes: <br><br><code>[linear_model]</code> &gt;&gt; <code>[model]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
     <div class="section level2">
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
-    <p>R Core Team (2023). <em>R: A Language and Environment for Statistical
+    <p>R Core Team (2024). <em>R: A Language and Environment for Statistical
 Computing</em>. R Foundation for Statistical Computing, Vienna, Austria.
 <a href="https://www.R-project.org/" class="external-link">https://www.R-project.org/</a>.</p>
     </div>
@@ -113,23 +101,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
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+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
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diff --git a/reference/log_transform.html b/reference/log_transform.html
index 21f00f6..57142f9 100644
--- a/reference/log_transform.html
+++ b/reference/log_transform.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="A logarithmic transform is applied to all values in the data matrix."><title>logarithm transform — log_transform • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="logarithm transform — log_transform"><meta property="og:description" content="A logarithmic transform is applied to all values in the data matrix."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-      <img src="" class="logo" alt=""><h1>logarithm transform</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/log_transform.R" class="external-link"><code>R/log_transform.R</code></a></small>
+
+      <h1>logarithm transform</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/log_transform.R" class="external-link"><code>R/log_transform.R</code></a></small>
       <div class="d-none name"><code>log_transform.Rd</code></div>
     </div>
 
@@ -59,23 +47,23 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>base</dt>
+
+
+<dl><dt id="arg-base">base<a class="anchor" aria-label="anchor" href="#arg-base"></a></dt>
 <dd><p>(numeric) The base of the logarithm used for the transform. The default is <code>10</code>.<br></p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>log_transform</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>transformed</code></td><td>(DatasetExperiment) A DatasetExperiment object containing the log transformed data.</td></tr></table><p>struct object</p>
+    <p>A  <code>log_transform</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>transformed</code></td><td>(DatasetExperiment) A DatasetExperiment object containing the log transformed data.</td></tr></table><p>struct object</p>
     </div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>log_transform</code> object inherits the following <code>struct</code> classes: <br><br><code>[log_transform]</code> &gt;&gt; <code>[model]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -88,23 +76,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">log_transform</span><span class="op">(</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
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-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
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diff --git a/reference/mean_centre.html b/reference/mean_centre.html
index 9fcf8ce..c48db0b 100644
--- a/reference/mean_centre.html
+++ b/reference/mean_centre.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="The mean sample is subtracted from all samples in the data matrix. The features in the centred matrix all have zero mean."><title>Mean centre — mean_centre • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Mean centre — mean_centre"><meta property="og:description" content="The mean sample is subtracted from all samples in the data matrix. The features in the centred matrix all have zero mean."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Mean centre — mean_centre • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Mean centre — mean_centre"><meta name="description" content="The mean sample is subtracted from all samples in the data matrix. The features in the centred matrix all have zero mean."><meta property="og:description" content="The mean sample is subtracted from all samples in the data matrix. The features in the centred matrix all have zero mean."></head><body>
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     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
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+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
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-      <img src="" class="logo" alt=""><h1>Mean centre</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/mean_centre_class.R" class="external-link"><code>R/mean_centre_class.R</code></a></small>
+
+      <h1>Mean centre</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/mean_centre_class.R" class="external-link"><code>R/mean_centre_class.R</code></a></small>
       <div class="d-none name"><code>mean_centre.Rd</code></div>
     </div>
 
@@ -59,25 +47,25 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>mode</dt>
+
+
+<dl><dt id="arg-mode">mode<a class="anchor" aria-label="anchor" href="#arg-mode"></a></dt>
 <dd><p>(character) Mode of action. Allowed values are limited to the following:</p><ul><li><p><code>"data"</code>: Centring is applied to the data block.</p></li>
 <li><p><code>"sample_meta"</code>: Centring is applied to the sample_meta block.</p></li>
 <li><p><code>"both"</code>: Centring is applied to both the data and the sample_meta blocks.</p></li>
 </ul><p>The default is <code>"data"</code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>mean_centre</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>centred</code></td><td>(DatasetExperiment)</td></tr><tr><td><code>mean_data</code></td><td>(numeric)</td></tr><tr><td><code>mean_sample_meta</code></td><td>(numeric)</td></tr></table></div>
+    <p>A  <code>mean_centre</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>centred</code></td><td>(DatasetExperiment)</td></tr><tr><td><code>mean_data</code></td><td>(numeric)</td></tr><tr><td><code>mean_sample_meta</code></td><td>(numeric)</td></tr></table></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>mean_centre</code> object inherits the following <code>struct</code> classes: <br><br><code>[mean_centre]</code> &gt;&gt; <code>[preprocess]</code> &gt;&gt; <code>[model]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -90,23 +78,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">mean_centre</span><span class="op">(</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
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diff --git a/reference/mean_of_medians.html b/reference/mean_of_medians.html
index e0c0792..ce0612a 100644
--- a/reference/mean_of_medians.html
+++ b/reference/mean_of_medians.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="The data matrix is normalised by the mean of the median of each factor level."><title>Mean of medians — mean_of_medians • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Mean of medians — mean_of_medians"><meta property="og:description" content="The data matrix is normalised by the mean of the median of each factor level."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Mean of medians — mean_of_medians • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Mean of medians — mean_of_medians"><meta name="description" content="The data matrix is normalised by the mean of the median of each factor level."><meta property="og:description" content="The data matrix is normalised by the mean of the median of each factor level."></head><body>
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     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
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+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
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+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Mean of medians</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/mean_of_medians.R" class="external-link"><code>R/mean_of_medians.R</code></a></small>
+
+      <h1>Mean of medians</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/mean_of_medians.R" class="external-link"><code>R/mean_of_medians.R</code></a></small>
       <div class="d-none name"><code>mean_of_medians.Rd</code></div>
     </div>
 
@@ -59,22 +47,22 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>factor_name</dt>
+
+
+<dl><dt id="arg-factor-name">factor_name<a class="anchor" aria-label="anchor" href="#arg-factor-name"></a></dt>
 <dd><p>(character) The name of a sample-meta column to use.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>mean_of_medians</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>transformed</code></td><td>(DatasetExperiment) Data after the tranformation has been applied.</td></tr></table></div>
+    <p>A  <code>mean_of_medians</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>transformed</code></td><td>(DatasetExperiment) Data after the tranformation has been applied.</td></tr></table></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>mean_of_medians</code> object inherits the following <code>struct</code> classes: <br><br><code>[mean_of_medians]</code> &gt;&gt; <code>[model]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -89,23 +77,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="model_apply.html">model_apply</a></span><span class="op">(</span><span class="va">M</span>,<span class="va">D</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
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+
 
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diff --git a/reference/mixed_effect.html b/reference/mixed_effect.html
index bb4c6fa..e6bcbc7 100644
--- a/reference/mixed_effect.html
+++ b/reference/mixed_effect.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="A mixed effects model is an extension of ANOVA where there are both fixed and random effects."><title>Mixed effects model — mixed_effect • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Mixed effects model — mixed_effect"><meta property="og:description" content="A mixed effects model is an extension of ANOVA where there are both fixed and random effects."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Mixed effects model — mixed_effect • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Mixed effects model — mixed_effect"><meta name="description" content="A mixed effects model is an extension of ANOVA where there are both fixed and random effects."><meta property="og:description" content="A mixed effects model is an extension of ANOVA where there are both fixed and random effects."></head><body>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Mixed effects model</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/mixed_effect_class.R" class="external-link"><code>R/mixed_effect_class.R</code></a></small>
+
+      <h1>Mixed effects model</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/mixed_effect_class.R" class="external-link"><code>R/mixed_effect_class.R</code></a></small>
       <div class="d-none name"><code>mixed_effect.Rd</code></div>
     </div>
 
@@ -59,36 +47,36 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>alpha</dt>
+
+
+<dl><dt id="arg-alpha">alpha<a class="anchor" aria-label="anchor" href="#arg-alpha"></a></dt>
 <dd><p>(numeric) The p-value cutoff for determining significance. The default is <code>0.05</code>.<br></p></dd>
 
 
-<dt>mtc</dt>
+<dt id="arg-mtc">mtc<a class="anchor" aria-label="anchor" href="#arg-mtc"></a></dt>
 <dd><p>(character) Multiple test correction method. Allowed values are limited to the following:</p><ul><li><p><code>"bonferroni"</code>: Bonferroni correction in which the p-values are multiplied by the number of comparisons.</p></li>
 <li><p><code>"fdr"</code>: Benjamini and Hochberg False Discovery Rate correction.</p></li>
 <li><p><code>"none"</code>: No correction.</p></li>
 </ul><p>The default is <code>"fdr"</code>.</p></dd>
 
 
-<dt>formula</dt>
+<dt id="arg-formula">formula<a class="anchor" aria-label="anchor" href="#arg-formula"></a></dt>
 <dd><p>(formula) A symbolic description of the model to be fitted.</p></dd>
 
 
-<dt>ss_type</dt>
+<dt id="arg-ss-type">ss_type<a class="anchor" aria-label="anchor" href="#arg-ss-type"></a></dt>
 <dd><p>(character) Sum of squares type. Allowed values are limited to the following:</p><ul><li><p><code>"marginal"</code>: Type III sum of squares.</p></li>
 <li><p><code>"sequential"</code>: Type II sum of squares.</p></li>
 </ul><p>The default is <code>"marginal"</code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>mixed_effect</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>f_statistic</code></td><td>(data.frame) The value of the calculated statistic.</td></tr><tr><td><code>p_value</code></td><td>(data.frame) The probability of observing the calculated statistic if the null hypothesis is true.</td></tr><tr><td><code>significant</code></td><td>(data.frame) True/False indicating whether the p-value computed for each variable is less than the threshold.</td></tr></table></div>
+    <p>A  <code>mixed_effect</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>f_statistic</code></td><td>(data.frame) The value of the calculated statistic.</td></tr><tr><td><code>p_value</code></td><td>(data.frame) The probability of observing the calculated statistic if the null hypothesis is true.</td></tr><tr><td><code>significant</code></td><td>(data.frame) True/False indicating whether the p-value computed for each variable is less than the threshold.</td></tr></table></div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>nlme</code></p></li>
@@ -96,7 +84,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 </ul></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>mixed_effect</code> object inherits the following <code>struct</code> classes: <br><br><code>[mixed_effect]</code> &gt;&gt; <code>[ANOVA]</code> &gt;&gt; <code>[model]</code> &gt;&gt; <code>[stato]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -108,8 +96,8 @@ <h2 id="references">References<a class="anchor" aria-label="anchor" href="#refer
 <p>Pinheiro JC, Bates DM (2000). <em>Mixed-Effects Models in S and S-PLUS</em>.
 Springer, New York. doi:10.1007/b98882
 <a href="https://doi.org/10.1007/b98882" class="external-link">https://doi.org/10.1007/b98882</a>.</p>
-<p>Lenth R (2023). <em>emmeans: Estimated Marginal Means, aka Least-Squares
-Means</em>. R package version 1.9.0,
+<p>Lenth R (2024). <em>emmeans: Estimated Marginal Means, aka Least-Squares
+Means</em>. R package version 1.10.1,
 <a href="https://CRAN.R-project.org/package=emmeans" class="external-link">https://CRAN.R-project.org/package=emmeans</a>.</p>
 <p>Fox J, Weisberg S (2019). <em>An R Companion to Applied Regression</em>, Third
 edition. Sage, Thousand Oaks CA.
@@ -130,23 +118,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="model_apply.html">model_apply</a></span><span class="op">(</span><span class="va">M</span>,<span class="va">D</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
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+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://computational-metabolomics.github.io/structToolbox/reference/model_apply.html" />
+    <meta name="robots" content="noindex">
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+
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+<html>
+  <head>
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+  <head>
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+    <meta http-equiv="refresh" content="0;URL=https://computational-metabolomics.github.io/structToolbox/reference/model_apply.html" />
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+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://computational-metabolomics.github.io/structToolbox/reference/model_apply.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://computational-metabolomics.github.io/structToolbox/reference/model_apply.html">
+  </head>
+</html>
+
diff --git a/reference/model_apply,tSNE,DatasetExperiment-method.html b/reference/model_apply,tSNE,DatasetExperiment-method.html
new file mode 100644
index 0000000..dcfbdc7
--- /dev/null
+++ b/reference/model_apply,tSNE,DatasetExperiment-method.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://computational-metabolomics.github.io/structToolbox/reference/model_apply.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://computational-metabolomics.github.io/structToolbox/reference/model_apply.html">
+  </head>
+</html>
+
diff --git a/reference/model_apply,ttest,DatasetExperiment-method.html b/reference/model_apply,ttest,DatasetExperiment-method.html
new file mode 100644
index 0000000..dcfbdc7
--- /dev/null
+++ b/reference/model_apply,ttest,DatasetExperiment-method.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://computational-metabolomics.github.io/structToolbox/reference/model_apply.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://computational-metabolomics.github.io/structToolbox/reference/model_apply.html">
+  </head>
+</html>
+
diff --git a/reference/model_apply,vec_norm,DatasetExperiment-method.html b/reference/model_apply,vec_norm,DatasetExperiment-method.html
new file mode 100644
index 0000000..dcfbdc7
--- /dev/null
+++ b/reference/model_apply,vec_norm,DatasetExperiment-method.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://computational-metabolomics.github.io/structToolbox/reference/model_apply.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://computational-metabolomics.github.io/structToolbox/reference/model_apply.html">
+  </head>
+</html>
+
diff --git a/reference/model_apply,wilcox_test,DatasetExperiment-method.html b/reference/model_apply,wilcox_test,DatasetExperiment-method.html
new file mode 100644
index 0000000..dcfbdc7
--- /dev/null
+++ b/reference/model_apply,wilcox_test,DatasetExperiment-method.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://computational-metabolomics.github.io/structToolbox/reference/model_apply.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://computational-metabolomics.github.io/structToolbox/reference/model_apply.html">
+  </head>
+</html>
+
diff --git a/reference/model_apply.html b/reference/model_apply.html
index 7cd2243..382f90d 100644
--- a/reference/model_apply.html
+++ b/reference/model_apply.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Applies method to the input DatasetExperiment"><title>Apply method — model_apply,ANOVA,DatasetExperiment-method • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Apply method — model_apply,ANOVA,DatasetExperiment-method"><meta property="og:description" content="Applies method to the input DatasetExperiment"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Apply method — model_apply,ANOVA,DatasetExperiment-method • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Apply method — model_apply,ANOVA,DatasetExperiment-method"><meta name="description" content="Applies method to the input DatasetExperiment"><meta property="og:description" content="Applies method to the input DatasetExperiment"></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary"><div class="container">
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+
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.17.3</small>
+
 
-    
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     <div id="navbar" class="collapse navbar-collapse ms-3">
-      <ul class="navbar-nav me-auto"><li class="active nav-item">
-  <a class="nav-link" href="../reference/index.html">Reference</a>
-</li>
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
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 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Apply method</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/anova_class.R" class="external-link"><code>R/anova_class.R</code></a>, <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/HSD_class.R" class="external-link"><code>R/HSD_class.R</code></a>, <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/mixed_effect_class.R" class="external-link"><code>R/mixed_effect_class.R</code></a>, and 27 more</small>
+
+      <h1>Apply method</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/anova_class.R" class="external-link"><code>R/anova_class.R</code></a>, <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/HSD_class.R" class="external-link"><code>R/HSD_class.R</code></a>, <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/mixed_effect_class.R" class="external-link"><code>R/mixed_effect_class.R</code></a>, and 27 more</small>
       <div class="d-none name"><code>model_apply.Rd</code></div>
     </div>
 
@@ -54,109 +42,109 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S4 method for ANOVA,DatasetExperiment</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S4 method for class 'ANOVA,DatasetExperiment'</span></span>
 <span><span class="fu">model_apply</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for HSD,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'HSD,DatasetExperiment'</span></span>
 <span><span class="fu">model_apply</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for mixed_effect,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'mixed_effect,DatasetExperiment'</span></span>
 <span><span class="fu">model_apply</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for HSDEM,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'HSDEM,DatasetExperiment'</span></span>
 <span><span class="fu">model_apply</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for classical_lsq,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'classical_lsq,DatasetExperiment'</span></span>
 <span><span class="fu">model_apply</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for confounders_clsq,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'confounders_clsq,DatasetExperiment'</span></span>
 <span><span class="fu">model_apply</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for constant_sum_norm,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'constant_sum_norm,DatasetExperiment'</span></span>
 <span><span class="fu">model_apply</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for corr_coef,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'corr_coef,DatasetExperiment'</span></span>
 <span><span class="fu">model_apply</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for split_data,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'split_data,DatasetExperiment'</span></span>
 <span><span class="fu">model_apply</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for equal_split,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'equal_split,DatasetExperiment'</span></span>
 <span><span class="fu">model_apply</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for filter_smeta,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'filter_smeta,DatasetExperiment'</span></span>
 <span><span class="fu">model_apply</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for fisher_exact,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'fisher_exact,DatasetExperiment'</span></span>
 <span><span class="fu">model_apply</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for fold_change,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'fold_change,DatasetExperiment'</span></span>
 <span><span class="fu">model_apply</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for fold_change_int,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'fold_change_int,DatasetExperiment'</span></span>
 <span><span class="fu">model_apply</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for HCA,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'HCA,DatasetExperiment'</span></span>
 <span><span class="fu">model_apply</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for knn_impute,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'knn_impute,DatasetExperiment'</span></span>
 <span><span class="fu">model_apply</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for kw_rank_sum,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'kw_rank_sum,DatasetExperiment'</span></span>
 <span><span class="fu">model_apply</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for log_transform,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'log_transform,DatasetExperiment'</span></span>
 <span><span class="fu">model_apply</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for mean_of_medians,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'mean_of_medians,DatasetExperiment'</span></span>
 <span><span class="fu">model_apply</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for nroot_transform,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'nroot_transform,DatasetExperiment'</span></span>
 <span><span class="fu">model_apply</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for pairs_filter,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'pairs_filter,DatasetExperiment'</span></span>
 <span><span class="fu">model_apply</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for prop_na,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'prop_na,DatasetExperiment'</span></span>
 <span><span class="fu">model_apply</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for rsd_filter,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'rsd_filter,DatasetExperiment'</span></span>
 <span><span class="fu">model_apply</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for sb_corr,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'sb_corr,DatasetExperiment'</span></span>
 <span><span class="fu">model_apply</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for stratified_split,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'stratified_split,DatasetExperiment'</span></span>
 <span><span class="fu">model_apply</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for tSNE,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'tSNE,DatasetExperiment'</span></span>
 <span><span class="fu">model_apply</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for ttest,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'ttest,DatasetExperiment'</span></span>
 <span><span class="fu">model_apply</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for vec_norm,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'vec_norm,DatasetExperiment'</span></span>
 <span><span class="fu">model_apply</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for wilcox_test,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'wilcox_test,DatasetExperiment'</span></span>
 <span><span class="fu">model_apply</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>M</dt>
+
+
+<dl><dt id="arg-m">M<a class="anchor" aria-label="anchor" href="#arg-m"></a></dt>
 <dd><p>a method object</p></dd>
 
 
-<dt>D</dt>
+<dt id="arg-d">D<a class="anchor" aria-label="anchor" href="#arg-d"></a></dt>
 <dd><p>another object used by the first</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>Returns a modified method object</p>
+    <p>Returns a modified method object</p>
     </div>
 
     <div class="section level2">
@@ -173,23 +161,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/model_predict,OPLSDA,DatasetExperiment-method.html b/reference/model_predict,OPLSDA,DatasetExperiment-method.html
new file mode 100644
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diff --git a/reference/model_predict,OPLSR,DatasetExperiment-method.html b/reference/model_predict,OPLSR,DatasetExperiment-method.html
new file mode 100644
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diff --git a/reference/model_predict,PCA,DatasetExperiment-method.html b/reference/model_predict,PCA,DatasetExperiment-method.html
new file mode 100644
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+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://computational-metabolomics.github.io/structToolbox/reference/model_predict.html" />
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diff --git a/reference/model_predict,PLSDA,DatasetExperiment-method.html b/reference/model_predict,PLSDA,DatasetExperiment-method.html
new file mode 100644
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+++ b/reference/model_predict,PLSDA,DatasetExperiment-method.html
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+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://computational-metabolomics.github.io/structToolbox/reference/model_predict.html" />
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diff --git a/reference/model_predict,PLSR,DatasetExperiment-method.html b/reference/model_predict,PLSR,DatasetExperiment-method.html
new file mode 100644
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new file mode 100644
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--- /dev/null
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diff --git a/reference/model_predict,autoscale,DatasetExperiment-method.html b/reference/model_predict,autoscale,DatasetExperiment-method.html
new file mode 100644
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+    <meta name="robots" content="noindex">
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diff --git a/reference/model_predict,blank_filter,DatasetExperiment-method.html b/reference/model_predict,blank_filter,DatasetExperiment-method.html
new file mode 100644
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+    <meta http-equiv="refresh" content="0;URL=https://computational-metabolomics.github.io/structToolbox/reference/model_predict.html" />
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diff --git a/reference/model_predict,constant_sum_norm,DatasetExperiment-method.html b/reference/model_predict,constant_sum_norm,DatasetExperiment-method.html
new file mode 100644
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+    <meta http-equiv="refresh" content="0;URL=https://computational-metabolomics.github.io/structToolbox/reference/model_predict.html" />
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diff --git a/reference/model_predict,dratio_filter,DatasetExperiment-method.html b/reference/model_predict,dratio_filter,DatasetExperiment-method.html
new file mode 100644
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+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://computational-metabolomics.github.io/structToolbox/reference/model_predict.html" />
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diff --git a/reference/model_predict,filter_by_name,DatasetExperiment-method.html b/reference/model_predict,filter_by_name,DatasetExperiment-method.html
new file mode 100644
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+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://computational-metabolomics.github.io/structToolbox/reference/model_predict.html" />
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+
diff --git a/reference/model_predict,filter_na_count,DatasetExperiment-method.html b/reference/model_predict,filter_na_count,DatasetExperiment-method.html
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+  <head>
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diff --git a/reference/model_predict,filter_smeta,DatasetExperiment-method.html b/reference/model_predict,filter_smeta,DatasetExperiment-method.html
new file mode 100644
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+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://computational-metabolomics.github.io/structToolbox/reference/model_predict.html" />
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+  </head>
+</html>
+
diff --git a/reference/model_predict,glog_transform,DatasetExperiment-method.html b/reference/model_predict,glog_transform,DatasetExperiment-method.html
new file mode 100644
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+++ b/reference/model_predict,glog_transform,DatasetExperiment-method.html
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+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://computational-metabolomics.github.io/structToolbox/reference/model_predict.html" />
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+  </head>
+</html>
+
diff --git a/reference/model_predict,linear_model,DatasetExperiment-method.html b/reference/model_predict,linear_model,DatasetExperiment-method.html
new file mode 100644
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+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://computational-metabolomics.github.io/structToolbox/reference/model_predict.html" />
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+
diff --git a/reference/model_predict,mean_centre,DatasetExperiment-method.html b/reference/model_predict,mean_centre,DatasetExperiment-method.html
new file mode 100644
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+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://computational-metabolomics.github.io/structToolbox/reference/model_predict.html" />
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+
diff --git a/reference/model_predict,mv_feature_filter,DatasetExperiment-method.html b/reference/model_predict,mv_feature_filter,DatasetExperiment-method.html
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@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://computational-metabolomics.github.io/structToolbox/reference/model_predict.html" />
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+</html>
+
diff --git a/reference/model_predict,mv_sample_filter,DatasetExperiment-method.html b/reference/model_predict,mv_sample_filter,DatasetExperiment-method.html
new file mode 100644
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@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://computational-metabolomics.github.io/structToolbox/reference/model_predict.html" />
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+</html>
+
diff --git a/reference/model_predict,pareto_scale,DatasetExperiment-method.html b/reference/model_predict,pareto_scale,DatasetExperiment-method.html
new file mode 100644
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+++ b/reference/model_predict,pareto_scale,DatasetExperiment-method.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
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+</html>
+
diff --git a/reference/model_predict,pqn_norm,DatasetExperiment-method.html b/reference/model_predict,pqn_norm,DatasetExperiment-method.html
new file mode 100644
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+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://computational-metabolomics.github.io/structToolbox/reference/model_predict.html" />
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+</html>
+
diff --git a/reference/model_predict,vec_norm,DatasetExperiment-method.html b/reference/model_predict,vec_norm,DatasetExperiment-method.html
new file mode 100644
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--- /dev/null
+++ b/reference/model_predict,vec_norm,DatasetExperiment-method.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
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+</html>
+
diff --git a/reference/model_predict.html b/reference/model_predict.html
index 10887e5..9579da0 100644
--- a/reference/model_predict.html
+++ b/reference/model_predict.html
@@ -1,52 +1,40 @@
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     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
       <span class="navbar-toggler-icon"></span>
     </button>
 
     <div id="navbar" class="collapse navbar-collapse ms-3">
-      <ul class="navbar-nav me-auto"><li class="active nav-item">
-  <a class="nav-link" href="../reference/index.html">Reference</a>
-</li>
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
-  <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-articles">Articles</a>
-  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
-    <a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a>
-  </div>
-</li>
-      </ul><form class="form-inline my-2 my-lg-0" role="search">
-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
-
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-  <a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="github">
-    <span class="fab fa fab fa-github fa-lg"></span>
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-</li>
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Model prediction</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/DFA_class.R" class="external-link"><code>R/DFA_class.R</code></a>, <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/PCA_class.R" class="external-link"><code>R/PCA_class.R</code></a>, <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/PLSR_class.R" class="external-link"><code>R/PLSR_class.R</code></a>, and 20 more</small>
+
+      <h1>Model prediction</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/DFA_class.R" class="external-link"><code>R/DFA_class.R</code></a>, <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/PCA_class.R" class="external-link"><code>R/PCA_class.R</code></a>, <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/PLSR_class.R" class="external-link"><code>R/PLSR_class.R</code></a>, and 20 more</small>
       <div class="d-none name"><code>model_predict.Rd</code></div>
     </div>
 
@@ -57,88 +45,88 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S4 method for DFA,DatasetExperiment</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S4 method for class 'DFA,DatasetExperiment'</span></span>
 <span><span class="fu">model_predict</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for PCA,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'PCA,DatasetExperiment'</span></span>
 <span><span class="fu">model_predict</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for PLSR,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'PLSR,DatasetExperiment'</span></span>
 <span><span class="fu">model_predict</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for PLSDA,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'PLSDA,DatasetExperiment'</span></span>
 <span><span class="fu">model_predict</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for autoscale,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'autoscale,DatasetExperiment'</span></span>
 <span><span class="fu">model_predict</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for blank_filter,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'blank_filter,DatasetExperiment'</span></span>
 <span><span class="fu">model_predict</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for constant_sum_norm,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'constant_sum_norm,DatasetExperiment'</span></span>
 <span><span class="fu">model_predict</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for dratio_filter,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'dratio_filter,DatasetExperiment'</span></span>
 <span><span class="fu">model_predict</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for filter_by_name,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'filter_by_name,DatasetExperiment'</span></span>
 <span><span class="fu">model_predict</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for filter_na_count,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'filter_na_count,DatasetExperiment'</span></span>
 <span><span class="fu">model_predict</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for filter_smeta,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'filter_smeta,DatasetExperiment'</span></span>
 <span><span class="fu">model_predict</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for glog_transform,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'glog_transform,DatasetExperiment'</span></span>
 <span><span class="fu">model_predict</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for linear_model,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'linear_model,DatasetExperiment'</span></span>
 <span><span class="fu">model_predict</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for mean_centre,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'mean_centre,DatasetExperiment'</span></span>
 <span><span class="fu">model_predict</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for mv_feature_filter,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'mv_feature_filter,DatasetExperiment'</span></span>
 <span><span class="fu">model_predict</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for mv_sample_filter,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'mv_sample_filter,DatasetExperiment'</span></span>
 <span><span class="fu">model_predict</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for OPLSR,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'OPLSR,DatasetExperiment'</span></span>
 <span><span class="fu">model_predict</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for OPLSDA,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'OPLSDA,DatasetExperiment'</span></span>
 <span><span class="fu">model_predict</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for pareto_scale,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'pareto_scale,DatasetExperiment'</span></span>
 <span><span class="fu">model_predict</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for pqn_norm,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'pqn_norm,DatasetExperiment'</span></span>
 <span><span class="fu">model_predict</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for SVM,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'SVM,DatasetExperiment'</span></span>
 <span><span class="fu">model_predict</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for vec_norm,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'vec_norm,DatasetExperiment'</span></span>
 <span><span class="fu">model_predict</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>M</dt>
+
+
+<dl><dt id="arg-m">M<a class="anchor" aria-label="anchor" href="#arg-m"></a></dt>
 <dd><p>a model object</p></dd>
 
 
-<dt>D</dt>
+<dt id="arg-d">D<a class="anchor" aria-label="anchor" href="#arg-d"></a></dt>
 <dd><p>a DatasetExperiment object</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>Returns a modified model object</p>
+    <p>Returns a modified model object</p>
     </div>
 
     <div class="section level2">
@@ -147,23 +135,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">model_predict</span><span class="op">(</span><span class="va">M</span>,<span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
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+
 
   </body></html>
 
diff --git a/reference/model_reverse,mean_centre,DatasetExperiment-method.html b/reference/model_reverse,mean_centre,DatasetExperiment-method.html
new file mode 100644
index 0000000..cbf674e
--- /dev/null
+++ b/reference/model_reverse,mean_centre,DatasetExperiment-method.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://computational-metabolomics.github.io/structToolbox/reference/model_reverse.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://computational-metabolomics.github.io/structToolbox/reference/model_reverse.html">
+  </head>
+</html>
+
diff --git a/reference/model_reverse.html b/reference/model_reverse.html
index b99643c..281db06 100644
--- a/reference/model_reverse.html
+++ b/reference/model_reverse.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Reverse the effect of a preprocessing step on a DatasetExperiment."><title>Reverse preprocessing — model_reverse,autoscale,DatasetExperiment-method • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Reverse preprocessing — model_reverse,autoscale,DatasetExperiment-method"><meta property="og:description" content="Reverse the effect of a preprocessing step on a DatasetExperiment."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Reverse preprocessing — model_reverse,autoscale,DatasetExperiment-method • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Reverse preprocessing — model_reverse,autoscale,DatasetExperiment-method"><meta name="description" content="Reverse the effect of a preprocessing step on a DatasetExperiment."><meta property="og:description" content="Reverse the effect of a preprocessing step on a DatasetExperiment."></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary"><div class="container">
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+
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.17.3</small>
+
 
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+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Reverse preprocessing</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/autoscale_class.R" class="external-link"><code>R/autoscale_class.R</code></a>, <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/mean_centre_class.R" class="external-link"><code>R/mean_centre_class.R</code></a>, <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/model_reverse_doc.R" class="external-link"><code>R/model_reverse_doc.R</code></a></small>
+
+      <h1>Reverse preprocessing</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/autoscale_class.R" class="external-link"><code>R/autoscale_class.R</code></a>, <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/mean_centre_class.R" class="external-link"><code>R/mean_centre_class.R</code></a>, <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/model_reverse_doc.R" class="external-link"><code>R/model_reverse_doc.R</code></a></small>
       <div class="d-none name"><code>model_reverse.Rd</code></div>
     </div>
 
@@ -54,28 +42,28 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S4 method for autoscale,DatasetExperiment</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S4 method for class 'autoscale,DatasetExperiment'</span></span>
 <span><span class="fu">model_reverse</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for mean_centre,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'mean_centre,DatasetExperiment'</span></span>
 <span><span class="fu">model_reverse</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>M</dt>
+
+
+<dl><dt id="arg-m">M<a class="anchor" aria-label="anchor" href="#arg-m"></a></dt>
 <dd><p>a model object</p></dd>
 
 
-<dt>D</dt>
+<dt id="arg-d">D<a class="anchor" aria-label="anchor" href="#arg-d"></a></dt>
 <dd><p>a DatasetExperiment object</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>Returns a modified DatasetExperiment object</p>
+    <p>Returns a modified DatasetExperiment object</p>
     </div>
 
     <div class="section level2">
@@ -84,23 +72,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">D</span> <span class="op">=</span> <span class="fu">model_reverse</span><span class="op">(</span><span class="va">M</span>,<span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/model_train,OPLSDA,DatasetExperiment-method.html b/reference/model_train,OPLSDA,DatasetExperiment-method.html
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Train a model — model_train,DFA,DatasetExperiment-method • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Train a model — model_train,DFA,DatasetExperiment-method"><meta name="description" content="Trains a model using the input DatasetExperiment"><meta property="og:description" content="Trains a model using the input DatasetExperiment"></head><body>
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     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.17.3</small>
+
 
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-      <ul class="navbar-nav me-auto"><li class="active nav-item">
-  <a class="nav-link" href="../reference/index.html">Reference</a>
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-      <img src="" class="logo" alt=""><h1>Train a model</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/DFA_class.R" class="external-link"><code>R/DFA_class.R</code></a>, <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/PCA_class.R" class="external-link"><code>R/PCA_class.R</code></a>, <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/PLSR_class.R" class="external-link"><code>R/PLSR_class.R</code></a>, and 20 more</small>
+
+      <h1>Train a model</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/DFA_class.R" class="external-link"><code>R/DFA_class.R</code></a>, <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/PCA_class.R" class="external-link"><code>R/PCA_class.R</code></a>, <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/PLSR_class.R" class="external-link"><code>R/PLSR_class.R</code></a>, and 20 more</small>
       <div class="d-none name"><code>model_train.Rd</code></div>
     </div>
 
@@ -54,88 +42,88 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S4 method for DFA,DatasetExperiment</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S4 method for class 'DFA,DatasetExperiment'</span></span>
 <span><span class="fu">model_train</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for PCA,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'PCA,DatasetExperiment'</span></span>
 <span><span class="fu">model_train</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for PLSR,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'PLSR,DatasetExperiment'</span></span>
 <span><span class="fu">model_train</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for PLSDA,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'PLSDA,DatasetExperiment'</span></span>
 <span><span class="fu">model_train</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for autoscale,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'autoscale,DatasetExperiment'</span></span>
 <span><span class="fu">model_train</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for blank_filter,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'blank_filter,DatasetExperiment'</span></span>
 <span><span class="fu">model_train</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for constant_sum_norm,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'constant_sum_norm,DatasetExperiment'</span></span>
 <span><span class="fu">model_train</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for dratio_filter,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'dratio_filter,DatasetExperiment'</span></span>
 <span><span class="fu">model_train</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for filter_by_name,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'filter_by_name,DatasetExperiment'</span></span>
 <span><span class="fu">model_train</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for filter_na_count,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'filter_na_count,DatasetExperiment'</span></span>
 <span><span class="fu">model_train</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for filter_smeta,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'filter_smeta,DatasetExperiment'</span></span>
 <span><span class="fu">model_train</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for glog_transform,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'glog_transform,DatasetExperiment'</span></span>
 <span><span class="fu">model_train</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for linear_model,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'linear_model,DatasetExperiment'</span></span>
 <span><span class="fu">model_train</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for mean_centre,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'mean_centre,DatasetExperiment'</span></span>
 <span><span class="fu">model_train</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for mv_feature_filter,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'mv_feature_filter,DatasetExperiment'</span></span>
 <span><span class="fu">model_train</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for mv_sample_filter,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'mv_sample_filter,DatasetExperiment'</span></span>
 <span><span class="fu">model_train</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for OPLSR,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'OPLSR,DatasetExperiment'</span></span>
 <span><span class="fu">model_train</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for OPLSDA,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'OPLSDA,DatasetExperiment'</span></span>
 <span><span class="fu">model_train</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for pareto_scale,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'pareto_scale,DatasetExperiment'</span></span>
 <span><span class="fu">model_train</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for pqn_norm,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'pqn_norm,DatasetExperiment'</span></span>
 <span><span class="fu">model_train</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for SVM,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'SVM,DatasetExperiment'</span></span>
 <span><span class="fu">model_train</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for vec_norm,DatasetExperiment</span></span>
+<span><span class="co"># S4 method for class 'vec_norm,DatasetExperiment'</span></span>
 <span><span class="fu">model_train</span><span class="op">(</span><span class="va">M</span>, <span class="va">D</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>M</dt>
+
+
+<dl><dt id="arg-m">M<a class="anchor" aria-label="anchor" href="#arg-m"></a></dt>
 <dd><p>a model object</p></dd>
 
 
-<dt>D</dt>
+<dt id="arg-d">D<a class="anchor" aria-label="anchor" href="#arg-d"></a></dt>
 <dd><p>a DatasetExperiment object</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>Returns a modified model object</p>
+    <p>Returns a modified model object</p>
     </div>
 
     <div class="section level2">
@@ -144,23 +132,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">model_train</span><span class="op">(</span><span class="va">M</span>,<span class="fu">iris_DatasetExperiment</span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
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diff --git a/reference/mv_boxplot-1.png b/reference/mv_boxplot-1.png
index 5533a62..96e3d40 100644
Binary files a/reference/mv_boxplot-1.png and b/reference/mv_boxplot-1.png differ
diff --git a/reference/mv_boxplot.html b/reference/mv_boxplot.html
index cc256d7..cbfabc5 100644
--- a/reference/mv_boxplot.html
+++ b/reference/mv_boxplot.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Boxplots of the number of missing values per sample/feature."><title>Missing value boxplots — mv_boxplot • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Missing value boxplots — mv_boxplot"><meta property="og:description" content="Boxplots of the number of missing values per sample/feature."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Missing value boxplots — mv_boxplot • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Missing value boxplots — mv_boxplot"><meta name="description" content="Boxplots of the number of missing values per sample/feature."><meta property="og:description" content="Boxplots of the number of missing values per sample/feature."></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
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+
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.17.3</small>
+
 
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-  <a class="nav-link" href="../reference/index.html">Reference</a>
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Missing value boxplots</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/dataset_chart_classes.R" class="external-link"><code>R/dataset_chart_classes.R</code></a></small>
+
+      <h1>Missing value boxplots</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/dataset_chart_classes.R" class="external-link"><code>R/dataset_chart_classes.R</code></a></small>
       <div class="d-none name"><code>mv_boxplot.Rd</code></div>
     </div>
 
@@ -65,44 +53,44 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>label_outliers</dt>
+
+
+<dl><dt id="arg-label-outliers">label_outliers<a class="anchor" aria-label="anchor" href="#arg-label-outliers"></a></dt>
 <dd><p>(logical) Label outliers. Allowed values are limited to the following:</p><ul><li><p><code>"TRUE"</code>: Sample labels for potential outliers are displayed on the plot.</p></li>
 <li><p><code>"FALSE"</code>: Sample labels are not included on the plot.</p></li>
 </ul><p>The default is <code>TRUE</code>.<br></p></dd>
 
 
-<dt>by_sample</dt>
+<dt id="arg-by-sample">by_sample<a class="anchor" aria-label="anchor" href="#arg-by-sample"></a></dt>
 <dd><p>(logical) Plot by sample or by feature. Allowed values are limited to the following:</p><ul><li><p><code>"TRUE"</code>: Missing values are plotted per sample.</p></li>
 <li><p><code>"FALSE"</code>: Missing values are plotted per feature.</p></li>
 </ul><p>The default is <code>TRUE</code>.<br></p></dd>
 
 
-<dt>factor_name</dt>
+<dt id="arg-factor-name">factor_name<a class="anchor" aria-label="anchor" href="#arg-factor-name"></a></dt>
 <dd><p>(character) The name of a sample-meta column to use.</p></dd>
 
 
-<dt>show_counts</dt>
+<dt id="arg-show-counts">show_counts<a class="anchor" aria-label="anchor" href="#arg-show-counts"></a></dt>
 <dd><p>(logical) Show counts. Allowed values are limited to the following:</p><ul><li><p><code>"TRUE"</code>: The number of samples for each box is displayed.</p></li>
 <li><p><code>"FALSE"</code>: The number of samples for each box is not displayed.</p></li>
 </ul><p>The default is <code>TRUE</code>.<br></p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>
+    <p>A  <code>
 mv_boxplot
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>mv_boxplot</code> object inherits the following <code>struct</code> classes: <br><br><code>[mv_boxplot]</code> &gt;&gt; <code>[chart]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -122,23 +110,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
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   </body></html>
 
diff --git a/reference/mv_feature_filter.html b/reference/mv_feature_filter.html
index e29225e..faf5c34 100644
--- a/reference/mv_feature_filter.html
+++ b/reference/mv_feature_filter.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Removes features where the percentage of non-missing values falls below a threshold."><title>Filter features by missing values — mv_feature_filter • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Filter features by missing values — mv_feature_filter"><meta property="og:description" content="Removes features where the percentage of non-missing values falls below a threshold."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Filter features by missing values — mv_feature_filter • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Filter features by missing values — mv_feature_filter"><meta name="description" content="Removes features where the percentage of non-missing values falls below a threshold."><meta property="og:description" content="Removes features where the percentage of non-missing values falls below a threshold."></head><body>
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     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.17.3</small>
+
 
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+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
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+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Filter features by missing values</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/mv_feature_filter_class.R" class="external-link"><code>R/mv_feature_filter_class.R</code></a></small>
+
+      <h1>Filter features by missing values</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/mv_feature_filter_class.R" class="external-link"><code>R/mv_feature_filter_class.R</code></a></small>
       <div class="d-none name"><code>mv_feature_filter.Rd</code></div>
     </div>
 
@@ -65,15 +53,17 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>threshold</dt>
+
+
+<dl><dt id="arg-threshold">threshold<a class="anchor" aria-label="anchor" href="#arg-threshold"></a></dt>
 <dd><p>(numeric) The minimum percentage of non-missing values. The default is <code>20</code>.<br></p></dd>
 
 
-<dt>qc_label</dt>
+<dt id="arg-qc-label">qc_label<a class="anchor" aria-label="anchor" href="#arg-qc-label"></a></dt>
 <dd><p>(character) The label used to identify QC/group samples when using the "QC" (within a named group) filtering method. The default is <code>"QC"</code>.</p></dd>
 
 
-<dt>method</dt>
+<dt id="arg-method">method<a class="anchor" aria-label="anchor" href="#arg-method"></a></dt>
 <dd><p>(character) Filtering method. Allowed values are limited to the following:</p><ul><li><p><code>"within_all"</code>: Features are removed if the threshold for non-missing values is not met for all groups.</p></li>
 <li><p><code>"within_one"</code>: Features are removed if the threshold for non-missing values is not met for any group.</p></li>
 <li><p><code>"QC"</code>: Features are removed if the threshold for non-missing values is not met for the named group.</p></li>
@@ -81,35 +71,32 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </ul><p>The default is <code>"QC"</code>.</p></dd>
 
 
-<dt>factor_name</dt>
+<dt id="arg-factor-name">factor_name<a class="anchor" aria-label="anchor" href="#arg-factor-name"></a></dt>
 <dd><p>(character) The name of a sample-meta column to use.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>mv_feature_filter</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>filtered</code></td><td>(DatasetExperiment) A DatasetExperiment object containing the filtered data.</td></tr><tr><td><code>flags</code></td><td>(data.frame) % missing values and a flag indicating whether the sample was rejected. 0 = rejected.</td></tr></table></div>
+    <p>A  <code>mv_feature_filter</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>filtered</code></td><td>(DatasetExperiment) A DatasetExperiment object containing the filtered data.</td></tr><tr><td><code>flags</code></td><td>(data.frame) % missing values and a flag indicating whether the sample was rejected. 0 = rejected.</td></tr></table></div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>pmp</code></p></li>
 </ul></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>mv_feature_filter</code> object inherits the following <code>struct</code> classes: <br><br><code>[mv_feature_filter]</code> &gt;&gt; <code>[model]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
     <div class="section level2">
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
-    <p>Jankevics A, Lloyd GR, Weber RJM (2023). <em>pmp: Peak Matrix Processing
-and signal batch correction for metabolomics datasets</em>.
-doi:10.18129/B9.bioc.pmp <a href="https://doi.org/10.18129/B9.bioc.pmp" class="external-link">https://doi.org/10.18129/B9.bioc.pmp</a>, R
-package version 1.14.0, <a href="https://bioconductor.org/packages/pmp" class="external-link">https://bioconductor.org/packages/pmp</a>.</p>
+    <p>Jankevics A, Lloyd GR, Weber RJM (????). <em>pmp: Peak Matrix Processing
+and signal batch correction for metabolomics datasets</em>. R package
+version 1.15.1.</p>
     </div>
 
     <div class="section level2">
@@ -125,23 +112,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="model_apply.html">model_apply</a></span><span class="op">(</span><span class="va">M</span>,<span class="va">D</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
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diff --git a/reference/mv_feature_filter_hist.html b/reference/mv_feature_filter_hist.html
index f3e2ea9..25fc36f 100644
--- a/reference/mv_feature_filter_hist.html
+++ b/reference/mv_feature_filter_hist.html
@@ -1,50 +1,38 @@
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     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
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 <li class="nav-item dropdown">
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 </nav><div class="container template-reference-topic">
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Histogram of missing values per feature</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/mv_feature_filter_class.R" class="external-link"><code>R/mv_feature_filter_class.R</code></a></small>
+
+      <h1>Histogram of missing values per feature</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/mv_feature_filter_class.R" class="external-link"><code>R/mv_feature_filter_class.R</code></a></small>
       <div class="d-none name"><code>mv_feature_filter_hist.Rd</code></div>
     </div>
 
@@ -59,22 +47,22 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>...</dt>
+
+
+<dl><dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>
+    <p>A  <code>
 mv_feature_filter_hist
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>mv_feature_filter_hist</code> object inherits the following <code>struct</code> classes: <br><br><code>[mv_feature_filter_hist]</code> &gt;&gt; <code>[chart]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -86,23 +74,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">mv_feature_filter_hist</span><span class="op">(</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
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diff --git a/reference/mv_histogram-1.png b/reference/mv_histogram-1.png
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diff --git a/reference/mv_histogram.html b/reference/mv_histogram.html
index 567f265..6eca224 100644
--- a/reference/mv_histogram.html
+++ b/reference/mv_histogram.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="A histogram of the numbers of missing values per sample/feature"><title>Missing value histogram — mv_histogram • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Missing value histogram — mv_histogram"><meta property="og:description" content="A histogram of the numbers of missing values per sample/feature"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Missing value histogram — mv_histogram • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Missing value histogram — mv_histogram"><meta name="description" content="A histogram of the numbers of missing values per sample/feature"><meta property="og:description" content="A histogram of the numbers of missing values per sample/feature"></head><body>
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     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
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+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.17.3</small>
+
 
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+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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 </nav><div class="container template-reference-topic">
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Missing value histogram</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/dataset_chart_classes.R" class="external-link"><code>R/dataset_chart_classes.R</code></a></small>
+
+      <h1>Missing value histogram</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/dataset_chart_classes.R" class="external-link"><code>R/dataset_chart_classes.R</code></a></small>
       <div class="d-none name"><code>mv_histogram.Rd</code></div>
     </div>
 
@@ -59,37 +47,35 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>label_outliers</dt>
+
+
+<dl><dt id="arg-label-outliers">label_outliers<a class="anchor" aria-label="anchor" href="#arg-label-outliers"></a></dt>
 <dd><p>(logical) Label outliers. Allowed values are limited to the following:</p><ul><li><p><code>"TRUE"</code>: Sample labels for potential outliers are displayed on the plot.</p></li>
 <li><p><code>"FALSE"</code>: Sample labels are not included on the plot.</p></li>
 </ul><p>The default is <code>TRUE</code>.<br></p></dd>
 
 
-<dt>by_sample</dt>
+<dt id="arg-by-sample">by_sample<a class="anchor" aria-label="anchor" href="#arg-by-sample"></a></dt>
 <dd><p>(logical) Plot by sample or by feature. Allowed values are limited to the following:</p><ul><li><p><code>"TRUE"</code>: Missing values are plotted per sample.</p></li>
 <li><p><code>"FALSE"</code>: Missing values are plotted per feature.</p></li>
 </ul><p>The default is <code>TRUE</code>.<br></p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>additional slots and values passed to struct_class</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>
+    <p>A  <code>
 mv_histogram
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
-
-
 <p>struct object</p>
     </div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>mv_histogram</code> object inherits the following <code>struct</code> classes: <br><br><code>[mv_histogram]</code> &gt;&gt; <code>[chart]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -104,28 +90,29 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">mv_histogram</span><span class="op">(</span>label_outliers<span class="op">=</span><span class="cn">FALSE</span>,by_sample<span class="op">=</span><span class="cn">FALSE</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="chart_plot.html">chart_plot</a></span><span class="op">(</span><span class="va">C</span>,<span class="va">D</span><span class="op">)</span></span></span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.</span>
-<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Removed 2 rows containing missing values (`geom_bar()`).</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Removed 2 rows containing missing values or values outside the scale range</span>
+<span class="r-wrn co"><span class="r-pr">#&gt;</span> (`geom_bar()`).</span>
 <span class="r-plt img"><img src="mv_histogram-1.png" alt="" width="700" height="433"></span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
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diff --git a/reference/mv_sample_filter.html b/reference/mv_sample_filter.html
index 6f79e36..1646d36 100644
--- a/reference/mv_sample_filter.html
+++ b/reference/mv_sample_filter.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Removes samples where the percent number of missing values exceeds a threshold."><title>Missing value sample filter — mv_sample_filter • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Missing value sample filter — mv_sample_filter"><meta property="og:description" content="Removes samples where the percent number of missing values exceeds a threshold."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Missing value sample filter — mv_sample_filter • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Missing value sample filter — mv_sample_filter"><meta name="description" content="Removes samples where the percent number of missing values exceeds a threshold."><meta property="og:description" content="Removes samples where the percent number of missing values exceeds a threshold."></head><body>
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Missing value sample filter</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/mv_sample_filter_class.R" class="external-link"><code>R/mv_sample_filter_class.R</code></a></small>
+
+      <h1>Missing value sample filter</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/mv_sample_filter_class.R" class="external-link"><code>R/mv_sample_filter_class.R</code></a></small>
       <div class="d-none name"><code>mv_sample_filter.Rd</code></div>
     </div>
 
@@ -59,35 +47,34 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>mv_threshold</dt>
+
+
+<dl><dt id="arg-mv-threshold">mv_threshold<a class="anchor" aria-label="anchor" href="#arg-mv-threshold"></a></dt>
 <dd><p>(numeric) The maximum percentage of features with missing values in a sample. The default is <code>20</code>.<br></p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>mv_sample_filter</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>filtered</code></td><td>(DatasetExperiment) A DatasetExperiment object containing the filtered data.</td></tr><tr><td><code>flags</code></td><td>(data.frame) A flag indicating whether the sample was rejected. 0 = rejected.</td></tr><tr><td><code>percent_missing</code></td><td>(data.frame) % missing values for each sample.</td></tr></table></div>
+    <p>A  <code>mv_sample_filter</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>filtered</code></td><td>(DatasetExperiment) A DatasetExperiment object containing the filtered data.</td></tr><tr><td><code>flags</code></td><td>(data.frame) A flag indicating whether the sample was rejected. 0 = rejected.</td></tr><tr><td><code>percent_missing</code></td><td>(data.frame) % missing values for each sample.</td></tr></table></div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>pmp</code></p></li>
 </ul></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>mv_sample_filter</code> object inherits the following <code>struct</code> classes: <br><br><code>[mv_sample_filter]</code> &gt;&gt; <code>[model]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
     <div class="section level2">
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
-    <p>Jankevics A, Lloyd GR, Weber RJM (2023). <em>pmp: Peak Matrix Processing
-and signal batch correction for metabolomics datasets</em>.
-doi:10.18129/B9.bioc.pmp <a href="https://doi.org/10.18129/B9.bioc.pmp" class="external-link">https://doi.org/10.18129/B9.bioc.pmp</a>, R
-package version 1.14.0, <a href="https://bioconductor.org/packages/pmp" class="external-link">https://bioconductor.org/packages/pmp</a>.</p>
+    <p>Jankevics A, Lloyd GR, Weber RJM (????). <em>pmp: Peak Matrix Processing
+and signal batch correction for metabolomics datasets</em>. R package
+version 1.15.1.</p>
     </div>
 
     <div class="section level2">
@@ -98,23 +85,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">mv_sample_filter</span><span class="op">(</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
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     </footer></div>
 
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-  
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diff --git a/reference/mv_sample_filter_hist.html b/reference/mv_sample_filter_hist.html
index a62a775..8f04938 100644
--- a/reference/mv_sample_filter_hist.html
+++ b/reference/mv_sample_filter_hist.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
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-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary"><div class="container">
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+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.17.3</small>
+
 
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-  <a class="nav-link" href="../reference/index.html">Reference</a>
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
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-    <a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a>
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-</li>
-      </ul><form class="form-inline my-2 my-lg-0" role="search">
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Histogram of missing values per sample</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/mv_sample_filter_class.R" class="external-link"><code>R/mv_sample_filter_class.R</code></a></small>
+
+      <h1>Histogram of missing values per sample</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/mv_sample_filter_class.R" class="external-link"><code>R/mv_sample_filter_class.R</code></a></small>
       <div class="d-none name"><code>mv_sample_filter_hist.Rd</code></div>
     </div>
 
@@ -59,22 +47,22 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>...</dt>
+
+
+<dl><dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>
+    <p>A  <code>
 mv_sample_filter_hist
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>mv_sample_filter_hist</code> object inherits the following <code>struct</code> classes: <br><br><code>[mv_sample_filter_hist]</code> &gt;&gt; <code>[chart]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -86,23 +74,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">mv_sample_filter_hist</span><span class="op">(</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
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diff --git a/reference/nroot_transform.html b/reference/nroot_transform.html
index 24e51aa..4515fad 100644
--- a/reference/nroot_transform.html
+++ b/reference/nroot_transform.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="All values in the data matrix are transformed by raising them to the power of 1/n."><title>nth root transform — nroot_transform • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="nth root transform — nroot_transform"><meta property="og:description" content="All values in the data matrix are transformed by raising them to the power of 1/n."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>nth root transform — nroot_transform • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="nth root transform — nroot_transform"><meta name="description" content="All values in the data matrix are transformed by raising them to the power of 1/n."><meta property="og:description" content="All values in the data matrix are transformed by raising them to the power of 1/n."></head><body>
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-    
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary"><div class="container">
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+
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.17.3</small>
+
 
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>nth root transform</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/nroot_transform_class.R" class="external-link"><code>R/nroot_transform_class.R</code></a></small>
+
+      <h1>nth root transform</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/nroot_transform_class.R" class="external-link"><code>R/nroot_transform_class.R</code></a></small>
       <div class="d-none name"><code>nroot_transform.Rd</code></div>
     </div>
 
@@ -59,22 +47,22 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>root</dt>
+
+
+<dl><dt id="arg-root">root<a class="anchor" aria-label="anchor" href="#arg-root"></a></dt>
 <dd><p>(numeric) The nth root used for the transform. The default is <code>2</code>.<br></p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>nroot_transform</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>transformed</code></td><td>(DatasetExperiment) A DatasetExperiment object containing the nth root transformed data.</td></tr></table></div>
+    <p>A  <code>nroot_transform</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>transformed</code></td><td>(DatasetExperiment) A DatasetExperiment object containing the nth root transformed data.</td></tr></table></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>nroot_transform</code> object inherits the following <code>struct</code> classes: <br><br><code>[nroot_transform]</code> &gt;&gt; <code>[model]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -87,23 +75,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">nroot_transform</span><span class="op">(</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
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-  
 
-  
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+
 
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diff --git a/reference/ontology_cache.html b/reference/ontology_cache.html
index f9bdfe5..fe77ecf 100644
--- a/reference/ontology_cache.html
+++ b/reference/ontology_cache.html
@@ -1,52 +1,40 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="A cached list of ontology terms obtained from the ontology lookup service
-(OLS) for ontology terms specified for objects in structToolbox."><title>ontology cache — ontology_cache • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="ontology cache — ontology_cache"><meta property="og:description" content="A cached list of ontology terms obtained from the ontology lookup service
-(OLS) for ontology terms specified for objects in structToolbox."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>ontology cache — ontology_cache • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="ontology cache — ontology_cache"><meta name="description" content="A cached list of ontology terms obtained from the ontology lookup service
+(OLS) for ontology terms specified for objects in structToolbox."><meta property="og:description" content="A cached list of ontology terms obtained from the ontology lookup service
+(OLS) for ontology terms specified for objects in structToolbox."></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary"><div class="container">
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+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.17.3</small>
+
 
-    
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
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-    <a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a>
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-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>ontology cache</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/zzz.R" class="external-link"><code>R/zzz.R</code></a></small>
+
+      <h1>ontology cache</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/zzz.R" class="external-link"><code>R/zzz.R</code></a></small>
       <div class="d-none name"><code>ontology_cache.Rd</code></div>
     </div>
 
@@ -62,9 +50,7 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>list of cached ontology terms</p>
+    <p>list of cached ontology terms</p>
     </div>
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
@@ -76,23 +62,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">cache</span> <span class="op">=</span> <span class="fu">ontology_cache</span><span class="op">(</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
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-  
 
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diff --git a/reference/pairs_filter.html b/reference/pairs_filter.html
index af26fe3..36bf495 100644
--- a/reference/pairs_filter.html
+++ b/reference/pairs_filter.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="This filter is used for study designs with paired sampling to ensure that  measurements from the same source (e.g. patient) are represented in all factor levels and interactions."><title>Pairs filter — pairs_filter • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Pairs filter — pairs_filter"><meta property="og:description" content="This filter is used for study designs with paired sampling to ensure that  measurements from the same source (e.g. patient) are represented in all factor levels and interactions."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Pairs filter — pairs_filter • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Pairs filter — pairs_filter"><meta name="description" content="This filter is used for study designs with paired sampling to ensure that  measurements from the same source (e.g. patient) are represented in all factor levels and interactions."><meta property="og:description" content="This filter is used for study designs with paired sampling to ensure that  measurements from the same source (e.g. patient) are represented in all factor levels and interactions."></head><body>
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-    
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary"><div class="container">
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+
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.17.3</small>
+
 
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
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-    <a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Pairs filter</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/pairs_filter_class.R" class="external-link"><code>R/pairs_filter_class.R</code></a></small>
+
+      <h1>Pairs filter</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/pairs_filter_class.R" class="external-link"><code>R/pairs_filter_class.R</code></a></small>
       <div class="d-none name"><code>pairs_filter.Rd</code></div>
     </div>
 
@@ -59,27 +47,27 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>factor_name</dt>
+
+
+<dl><dt id="arg-factor-name">factor_name<a class="anchor" aria-label="anchor" href="#arg-factor-name"></a></dt>
 <dd><p>(character) The name of a sample-meta column to use.</p></dd>
 
 
-<dt>sample_id</dt>
+<dt id="arg-sample-id">sample_id<a class="anchor" aria-label="anchor" href="#arg-sample-id"></a></dt>
 <dd><p>(character) Name of sample meta column containing sample identifiers.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>pairs_filter</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>filtered</code></td><td>(DatasetExperiment) A DatasetExperiment object after the filter has been applied.</td></tr><tr><td><code>flags</code></td><td>(data.frame) A data.frame indicating whether features were filtered from the DatasetExperiment.</td></tr></table><p>struct object</p>
+    <p>A  <code>pairs_filter</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>filtered</code></td><td>(DatasetExperiment) A DatasetExperiment object after the filter has been applied.</td></tr><tr><td><code>flags</code></td><td>(data.frame) A data.frame indicating whether features were filtered from the DatasetExperiment.</td></tr></table><p>struct object</p>
     </div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>pairs_filter</code> object inherits the following <code>struct</code> classes: <br><br><code>[pairs_filter]</code> &gt;&gt; <code>[model]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -93,23 +81,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">M</span><span class="op">=</span><span class="fu">pairs_filter</span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'Class'</span>,sample_id<span class="op">=</span><span class="st">'ids'</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
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diff --git a/reference/pareto_scale.html b/reference/pareto_scale.html
index 94ab3f5..fa35694 100644
--- a/reference/pareto_scale.html
+++ b/reference/pareto_scale.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
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     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
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-      <img src="" class="logo" alt=""><h1>Pareto scaling</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/paretoscale_class.R" class="external-link"><code>R/paretoscale_class.R</code></a></small>
+
+      <h1>Pareto scaling</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/paretoscale_class.R" class="external-link"><code>R/paretoscale_class.R</code></a></small>
       <div class="d-none name"><code>pareto_scale.Rd</code></div>
     </div>
 
@@ -59,18 +47,18 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>...</dt>
+
+
+<dl><dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>pareto_scale</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>scaled</code></td><td>(DatasetExperiment)</td></tr><tr><td><code>mean</code></td><td>(numeric)</td></tr><tr><td><code>sd</code></td><td>(numeric)</td></tr></table></div>
+    <p>A  <code>pareto_scale</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>scaled</code></td><td>(DatasetExperiment)</td></tr><tr><td><code>mean</code></td><td>(numeric)</td></tr><tr><td><code>sd</code></td><td>(numeric)</td></tr></table></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>pareto_scale</code> object inherits the following <code>struct</code> classes: <br><br><code>[pareto_scale]</code> &gt;&gt; <code>[model]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -85,23 +73,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu"><a href="model_predict.html">model_predict</a></span><span class="op">(</span><span class="va">M</span>,<span class="va">D</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
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-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
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diff --git a/reference/pca_biplot.html b/reference/pca_biplot.html
index 7102226..0ffb551 100644
--- a/reference/pca_biplot.html
+++ b/reference/pca_biplot.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="A scatter plot of the selected principal component scores overlaid with the corresponding principal component loadings."><title>PCA biplot — pca_biplot • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="PCA biplot — pca_biplot"><meta property="og:description" content="A scatter plot of the selected principal component scores overlaid with the corresponding principal component loadings."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>PCA biplot — pca_biplot • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="PCA biplot — pca_biplot"><meta name="description" content="A scatter plot of the selected principal component scores overlaid with the corresponding principal component loadings."><meta property="og:description" content="A scatter plot of the selected principal component scores overlaid with the corresponding principal component loadings."></head><body>
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     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
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+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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 </nav><div class="container template-reference-topic">
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>PCA biplot</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/PCA_plotfcns.R" class="external-link"><code>R/PCA_plotfcns.R</code></a></small>
+
+      <h1>PCA biplot</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/PCA_plotfcns.R" class="external-link"><code>R/PCA_plotfcns.R</code></a></small>
       <div class="d-none name"><code>pca_biplot.Rd</code></div>
     </div>
 
@@ -67,53 +55,53 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>components</dt>
+
+
+<dl><dt id="arg-components">components<a class="anchor" aria-label="anchor" href="#arg-components"></a></dt>
 <dd><p>(numeric) The principal components used to generate the plot. The default is <code>c(1, 2)</code>.</p></dd>
 
 
-<dt>points_to_label</dt>
+<dt id="arg-points-to-label">points_to_label<a class="anchor" aria-label="anchor" href="#arg-points-to-label"></a></dt>
 <dd><p>(character) points_to_label. Allowed values are limited to the following:</p><ul><li><p><code>"none"</code>: No samples are labelled on the plot.</p></li>
 <li><p><code>"all"</code>: All samples are labelled on the plot.</p></li>
 <li><p><code>"outliers"</code>: Potential outliers are labelled on the plot.</p></li>
 </ul><p>The default is <code>"none"</code>.</p></dd>
 
 
-<dt>factor_name</dt>
+<dt id="arg-factor-name">factor_name<a class="anchor" aria-label="anchor" href="#arg-factor-name"></a></dt>
 <dd><p>(character) The name of a sample-meta column to use.</p></dd>
 
 
-<dt>scale_factor</dt>
+<dt id="arg-scale-factor">scale_factor<a class="anchor" aria-label="anchor" href="#arg-scale-factor"></a></dt>
 <dd><p>(numeric) The scaling factor applied to the loadings. The default is <code>0.95</code>.<br></p></dd>
 
 
-<dt>style</dt>
+<dt id="arg-style">style<a class="anchor" aria-label="anchor" href="#arg-style"></a></dt>
 <dd><p>(character) Plot style. Allowed values are limited to the following:</p><ul><li><p><code>"points"</code>: Loadings and scores are plotted as a scatter plot.</p></li>
 <li><p><code>"arrows"</code>: The loadings are plotted as arrow vectors.</p></li>
 </ul><p>The default is <code>"points"</code>.</p></dd>
 
 
-<dt>label_features</dt>
+<dt id="arg-label-features">label_features<a class="anchor" aria-label="anchor" href="#arg-label-features"></a></dt>
 <dd><p>(logical) Add feature labels. Allowed values are limited to the following:</p><ul><li><p><code>"TRUE"</code>: Features are labelled.</p></li>
 <li><p><code>"FALSE"</code>: Features are not labelled.</p></li>
 </ul><p>The default is <code>FALSE</code>.<br></p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>
+    <p>A  <code>
 pca_biplot
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>pca_biplot</code> object inherits the following <code>struct</code> classes: <br><br><code>[pca_biplot]</code> &gt;&gt; <code>[chart]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -131,23 +119,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">pca_biplot</span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'Species'</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/pca_correlation_plot.html b/reference/pca_correlation_plot.html
index 0990089..625138c 100644
--- a/reference/pca_correlation_plot.html
+++ b/reference/pca_correlation_plot.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="A plot of the correlation between the variables/features and the selected principal component scores. Features with high correlation are well represented by the selected component(s)"><title>PCA correlation plot — pca_correlation_plot • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="PCA correlation plot — pca_correlation_plot"><meta property="og:description" content="A plot of the correlation between the variables/features and the selected principal component scores. Features with high correlation are well represented by the selected component(s)"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>PCA correlation plot — pca_correlation_plot • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="PCA correlation plot — pca_correlation_plot"><meta name="description" content="A plot of the correlation between the variables/features and the selected principal component scores. Features with high correlation are well represented by the selected component(s)"><meta property="og:description" content="A plot of the correlation between the variables/features and the selected principal component scores. Features with high correlation are well represented by the selected component(s)"></head><body>
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-    
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary"><div class="container">
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     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.17.3</small>
+
 
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>PCA correlation plot</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/PCA_plotfcns.R" class="external-link"><code>R/PCA_plotfcns.R</code></a></small>
+
+      <h1>PCA correlation plot</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/PCA_plotfcns.R" class="external-link"><code>R/PCA_plotfcns.R</code></a></small>
       <div class="d-none name"><code>pca_correlation_plot.Rd</code></div>
     </div>
 
@@ -59,26 +47,26 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>components</dt>
+
+
+<dl><dt id="arg-components">components<a class="anchor" aria-label="anchor" href="#arg-components"></a></dt>
 <dd><p>(numeric) The Principal Components used to generate the plot. The default is <code>c(1, 2)</code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>
+    <p>A  <code>
 pca_correlation_plot
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>pca_correlation_plot</code> object inherits the following <code>struct</code> classes: <br><br><code>[pca_correlation_plot]</code> &gt;&gt; <code>[chart]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -91,23 +79,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">pca_correlation_plot</span><span class="op">(</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/pca_dstat_plot.html b/reference/pca_dstat_plot.html
index 742dcf4..15cdba7 100644
--- a/reference/pca_dstat_plot.html
+++ b/reference/pca_dstat_plot.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="A bar chart of the d-statistics for samples in the input PCA model. Samples above the indicated threshold are considered to be outlying."><title>d-statistic plot — pca_dstat_plot • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="d-statistic plot — pca_dstat_plot"><meta property="og:description" content="A bar chart of the d-statistics for samples in the input PCA model. Samples above the indicated threshold are considered to be outlying."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
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-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>d-statistic plot — pca_dstat_plot • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="d-statistic plot — pca_dstat_plot"><meta name="description" content="A bar chart of the d-statistics for samples in the input PCA model. Samples above the indicated threshold are considered to be outlying."><meta property="og:description" content="A bar chart of the d-statistics for samples in the input PCA model. Samples above the indicated threshold are considered to be outlying."></head><body>
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.17.3</small>
+
 
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>d-statistic plot</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/PCA_plotfcns.R" class="external-link"><code>R/PCA_plotfcns.R</code></a></small>
+
+      <h1>d-statistic plot</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/PCA_plotfcns.R" class="external-link"><code>R/PCA_plotfcns.R</code></a></small>
       <div class="d-none name"><code>pca_dstat_plot.Rd</code></div>
     </div>
 
@@ -59,30 +47,30 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>number_components</dt>
+
+
+<dl><dt id="arg-number-components">number_components<a class="anchor" aria-label="anchor" href="#arg-number-components"></a></dt>
 <dd><p>(numeric) The number of principal components to use. The default is <code>2</code>.<br></p></dd>
 
 
-<dt>alpha</dt>
+<dt id="arg-alpha">alpha<a class="anchor" aria-label="anchor" href="#arg-alpha"></a></dt>
 <dd><p>(numeric) A confidence threshold for rejecting samples based on the d-statistic. The default is <code>0.05</code>.<br></p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>
+    <p>A  <code>
 pca_dstat_plot
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>pca_dstat_plot</code> object inherits the following <code>struct</code> classes: <br><br><code>[pca_dstat_plot]</code> &gt;&gt; <code>[chart]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -96,23 +84,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">pca_dstat_plot</span><span class="op">(</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
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diff --git a/reference/pca_loadings_plot.html b/reference/pca_loadings_plot.html
index 0cd2214..4e54c5b 100644
--- a/reference/pca_loadings_plot.html
+++ b/reference/pca_loadings_plot.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="A barchart (one component) or scatter plot (two components) of the selected principal component loadings."><title>PCA loadings plot — pca_loadings_plot • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="PCA loadings plot — pca_loadings_plot"><meta property="og:description" content="A barchart (one component) or scatter plot (two components) of the selected principal component loadings."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>PCA loadings plot — pca_loadings_plot • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="PCA loadings plot — pca_loadings_plot"><meta name="description" content="A barchart (one component) or scatter plot (two components) of the selected principal component loadings."><meta property="og:description" content="A barchart (one component) or scatter plot (two components) of the selected principal component loadings."></head><body>
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+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
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+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>PCA loadings plot</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/PCA_plotfcns.R" class="external-link"><code>R/PCA_plotfcns.R</code></a></small>
+
+      <h1>PCA loadings plot</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/PCA_plotfcns.R" class="external-link"><code>R/PCA_plotfcns.R</code></a></small>
       <div class="d-none name"><code>pca_loadings_plot.Rd</code></div>
     </div>
 
@@ -64,39 +52,39 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>components</dt>
+
+
+<dl><dt id="arg-components">components<a class="anchor" aria-label="anchor" href="#arg-components"></a></dt>
 <dd><p>(numeric) The principal components used to generate the plot. The default is <code>c(1, 2)</code>.</p></dd>
 
 
-<dt>style</dt>
+<dt id="arg-style">style<a class="anchor" aria-label="anchor" href="#arg-style"></a></dt>
 <dd><p>(character) Plot style. Allowed values are limited to the following:</p><ul><li><p><code>"points"</code>: Loadings and scores are plotted as a scatter plot.</p></li>
 <li><p><code>"arrows"</code>: The loadings are plotted as arrow vectors.</p></li>
 </ul><p>The default is <code>"points"</code>.</p></dd>
 
 
-<dt>label_features</dt>
+<dt id="arg-label-features">label_features<a class="anchor" aria-label="anchor" href="#arg-label-features"></a></dt>
 <dd><p>(character, NULL) Feature labels. Allowed values are limited to the following:</p><ul><li><p><code>"character()"</code>: A vector of labels for the features.</p></li>
 <li><p><code>"NULL"</code>: No labels.</p></li>
 <li><p><code>"row.names"</code>: Labels will be extracted from the column names of the data matrix.</p></li>
 </ul><p>The default is <code>NULL</code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>
+    <p>A  <code>
 pca_loadings_plot
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>pca_loadings_plot</code> object inherits the following <code>struct</code> classes: <br><br><code>[pca_loadings_plot]</code> &gt;&gt; <code>[chart]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -111,23 +99,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">pca_loadings_plot</span><span class="op">(</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/pca_scores_plot-1.png b/reference/pca_scores_plot-1.png
index 0ea2c7c..8d39c68 100644
Binary files a/reference/pca_scores_plot-1.png and b/reference/pca_scores_plot-1.png differ
diff --git a/reference/pca_scores_plot.html b/reference/pca_scores_plot.html
index 965abf2..d843956 100644
--- a/reference/pca_scores_plot.html
+++ b/reference/pca_scores_plot.html
@@ -1,50 +1,38 @@
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-      <img src="" class="logo" alt=""><h1>PCA scores plot</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/PCA_plotfcns.R" class="external-link"><code>R/PCA_plotfcns.R</code></a></small>
+
+      <h1>PCA scores plot</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/PCA_plotfcns.R" class="external-link"><code>R/PCA_plotfcns.R</code></a></small>
       <div class="d-none name"><code>pca_scores_plot.Rd</code></div>
     </div>
 
@@ -72,26 +60,28 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>xcol</dt>
+
+
+<dl><dt id="arg-xcol">xcol<a class="anchor" aria-label="anchor" href="#arg-xcol"></a></dt>
 <dd><p>(numeric, integer, character) The column name, or index, of data to plot on the x-axis. The default is <code>"PC1"</code>.</p></dd>
 
 
-<dt>ycol</dt>
+<dt id="arg-ycol">ycol<a class="anchor" aria-label="anchor" href="#arg-ycol"></a></dt>
 <dd><p>(numeric, integer, character) The column name, or index, of data to plot on the y-axis. The default is <code>"PC2"</code>.</p></dd>
 
 
-<dt>points_to_label</dt>
+<dt id="arg-points-to-label">points_to_label<a class="anchor" aria-label="anchor" href="#arg-points-to-label"></a></dt>
 <dd><p>(character) Points to label. Allowed values are limited to the following:</p><ul><li><p><code>"none"</code>: No samples labels are displayed.</p></li>
 <li><p><code>"all"</code>: The labels for all samples are displayed.</p></li>
 <li><p><code>"outliers"</code>: Labels for for potential outlier samples are displayed.</p></li>
 </ul><p>The default is <code>"none"</code>.</p></dd>
 
 
-<dt>factor_name</dt>
+<dt id="arg-factor-name">factor_name<a class="anchor" aria-label="anchor" href="#arg-factor-name"></a></dt>
 <dd><p>(character) The name of a sample-meta column to use.</p></dd>
 
 
-<dt>ellipse</dt>
+<dt id="arg-ellipse">ellipse<a class="anchor" aria-label="anchor" href="#arg-ellipse"></a></dt>
 <dd><p>(character) Plot ellipses. Allowed values are limited to the following:</p><ul><li><p><code>"all"</code>: Ellipses are plotted for all groups and all samples.</p></li>
 <li><p><code>"group"</code>: Ellipses are plotted for all groups.</p></li>
 <li><p><code>"none"</code>: Ellipses are not included on the plot.</p></li>
@@ -99,48 +89,46 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </ul><p>The default is <code>"all"</code>.</p></dd>
 
 
-<dt>ellipse_type</dt>
+<dt id="arg-ellipse-type">ellipse_type<a class="anchor" aria-label="anchor" href="#arg-ellipse-type"></a></dt>
 <dd><p>(character) Type of ellipse. Allowed values are limited to the following:</p><ul><li><p><code>"norm"</code>: Multivariate normal (p = 0.95).</p></li>
 <li><p><code>"t"</code>: Multivariate t (p = 0.95).</p></li>
 </ul><p>The default is <code>"norm"</code>.</p></dd>
 
 
-<dt>ellipse_confidence</dt>
+<dt id="arg-ellipse-confidence">ellipse_confidence<a class="anchor" aria-label="anchor" href="#arg-ellipse-confidence"></a></dt>
 <dd><p>(numeric) The confidence level for plotting ellipses. The default is <code>0.95</code>.<br></p></dd>
 
 
-<dt>label_filter</dt>
+<dt id="arg-label-filter">label_filter<a class="anchor" aria-label="anchor" href="#arg-label-filter"></a></dt>
 <dd><p>(character) Labels are only plotted for the named groups. If zero-length then all groups are included. The default is <code>character(0)</code>.</p></dd>
 
 
-<dt>label_factor</dt>
+<dt id="arg-label-factor">label_factor<a class="anchor" aria-label="anchor" href="#arg-label-factor"></a></dt>
 <dd><p>(character) The column name of sample_meta to use for labelling samples on the plot. "rownames" will use the row names from sample_meta. The default is <code>"rownames"</code>.</p></dd>
 
 
-<dt>label_size</dt>
+<dt id="arg-label-size">label_size<a class="anchor" aria-label="anchor" href="#arg-label-size"></a></dt>
 <dd><p>(numeric) The text size of labels. Note this is not in Font Units. The default is <code>3.88</code>.<br></p></dd>
 
 
-<dt>components</dt>
+<dt id="arg-components">components<a class="anchor" aria-label="anchor" href="#arg-components"></a></dt>
 <dd><p>(numeric, integer, NULL) The principal components used to generate the plot. If provided this parameter overrides xcol and ycol params. The default is <code>NULL</code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>
+    <p>A  <code>
 pca_scores_plot
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>pca_scores_plot</code> object inherits the following <code>struct</code> classes: <br><br><code>[pca_scores_plot]</code> &gt;&gt; <code>[scatter_chart]</code> &gt;&gt; <code>[chart]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -168,23 +156,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-plt img"><img src="pca_scores_plot-1.png" alt="" width="700" height="433"></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
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diff --git a/reference/pca_scree_plot.html b/reference/pca_scree_plot.html
index 0e6cca2..8ae6d1c 100644
--- a/reference/pca_scree_plot.html
+++ b/reference/pca_scree_plot.html
@@ -1,50 +1,38 @@
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-  </div>
-</li>
-      </ul><form class="form-inline my-2 my-lg-0" role="search">
-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
-
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-  <a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="github">
-    <span class="fab fa fab fa-github fa-lg"></span>
-     
-  </a>
-</li>
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Scree plot</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/PCA_plotfcns.R" class="external-link"><code>R/PCA_plotfcns.R</code></a></small>
+
+      <h1>Scree plot</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/PCA_plotfcns.R" class="external-link"><code>R/PCA_plotfcns.R</code></a></small>
       <div class="d-none name"><code>pca_scree_plot.Rd</code></div>
     </div>
 
@@ -59,29 +47,27 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>max_pc</dt>
+
+
+<dl><dt id="arg-max-pc">max_pc<a class="anchor" aria-label="anchor" href="#arg-max-pc"></a></dt>
 <dd><p>(numeric, integer) The maximum number of components to include in the plot. The default is <code>15</code>.<br></p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>
+    <p>A  <code>
 pca_scree_plot
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
-
-
 <p>struct object</p>
     </div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>pca_scree_plot</code> object inherits the following <code>struct</code> classes: <br><br><code>[pca_scree_plot]</code> &gt;&gt; <code>[chart]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -94,23 +80,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">pca_scree_plot</span><span class="op">(</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/permutation_test.html b/reference/permutation_test.html
index 13545ab..7c3dc3c 100644
--- a/reference/permutation_test.html
+++ b/reference/permutation_test.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content='A permutation test generates a "null" model by randomising the response (for regression models) or group labels (for classification models). This is repeated many times to generate a distribution of performance metrics for the null model. This distribution can then be compared to the performance of the true model. If there is overlap between the true and null model performances then the model is overfitted.'><title>Permutation test — permutation_test • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Permutation test — permutation_test"><meta property="og:description" content='A permutation test generates a "null" model by randomising the response (for regression models) or group labels (for classification models). This is repeated many times to generate a distribution of performance metrics for the null model. This distribution can then be compared to the performance of the true model. If there is overlap between the true and null model performances then the model is overfitted.'><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Permutation test — permutation_test • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Permutation test — permutation_test"><meta name="description" content='A permutation test generates a "null" model by randomising the response (for regression models) or group labels (for classification models). This is repeated many times to generate a distribution of performance metrics for the null model. This distribution can then be compared to the performance of the true model. If there is overlap between the true and null model performances then the model is overfitted.'><meta property="og:description" content='A permutation test generates a "null" model by randomising the response (for regression models) or group labels (for classification models). This is repeated many times to generate a distribution of performance metrics for the null model. This distribution can then be compared to the performance of the true model. If there is overlap between the true and null model performances then the model is overfitted.'></head><body>
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+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
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 </nav><div class="container template-reference-topic">
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Permutation test</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/permutation_test_class.R" class="external-link"><code>R/permutation_test_class.R</code></a></small>
+
+      <h1>Permutation test</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/permutation_test_class.R" class="external-link"><code>R/permutation_test_class.R</code></a></small>
       <div class="d-none name"><code>permutation_test.Rd</code></div>
     </div>
 
@@ -59,26 +47,26 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>number_of_permutations</dt>
+
+
+<dl><dt id="arg-number-of-permutations">number_of_permutations<a class="anchor" aria-label="anchor" href="#arg-number-of-permutations"></a></dt>
 <dd><p>(numeric, integer) The number of permutations. The default is <code>50</code>.<br></p></dd>
 
 
-<dt>factor_name</dt>
+<dt id="arg-factor-name">factor_name<a class="anchor" aria-label="anchor" href="#arg-factor-name"></a></dt>
 <dd><p>(character) The name of a sample-meta column to use.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>permutation_test</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>results.permuted</code></td><td>(data.frame)</td></tr><tr><td><code>results.unpermuted</code></td><td>(data.frame)</td></tr><tr><td><code>metric</code></td><td>(data.frame)</td></tr></table></div>
+    <p>A  <code>permutation_test</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>results.permuted</code></td><td>(data.frame)</td></tr><tr><td><code>results.unpermuted</code></td><td>(data.frame)</td></tr><tr><td><code>metric</code></td><td>(data.frame)</td></tr></table></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>permutation_test</code> object inherits the following <code>struct</code> classes: <br><br><code>[permutation_test]</code> &gt;&gt; <code>[resampler]</code> &gt;&gt; <code>[iterator]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -92,23 +80,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">I</span><span class="op">=</span><span class="fu">permutation_test</span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'Species'</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
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diff --git a/reference/permutation_test_plot.html b/reference/permutation_test_plot.html
index 0ed35db..89084d5 100644
--- a/reference/permutation_test_plot.html
+++ b/reference/permutation_test_plot.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Plots the results of a permutation test."><title>permutation_test_plot class — permutation_test_plot • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="permutation_test_plot class — permutation_test_plot"><meta property="og:description" content="Plots the results of a permutation test."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-      <img src="" class="logo" alt=""><h1>permutation_test_plot class</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/permutation_test_class.R" class="external-link"><code>R/permutation_test_class.R</code></a></small>
+
+      <h1>permutation_test_plot class</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/permutation_test_class.R" class="external-link"><code>R/permutation_test_class.R</code></a></small>
       <div class="d-none name"><code>permutation_test_plot.Rd</code></div>
     </div>
 
@@ -59,23 +47,23 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>style</dt>
+
+
+<dl><dt id="arg-style">style<a class="anchor" aria-label="anchor" href="#arg-style"></a></dt>
 <dd><p>The plot style. One of 'boxplot', 'violin', 'histogram', 'density' or 'scatter'.</p></dd>
 
 
-<dt>binwidth</dt>
+<dt id="arg-binwidth">binwidth<a class="anchor" aria-label="anchor" href="#arg-binwidth"></a></dt>
 <dd><p>Binwidth for the "histogram" style. Ignored for all other styles.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>additional slots and values passed to struct_class</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>struct object</p>
+    <p>struct object</p>
     </div>
 
     <div class="section level2">
@@ -83,23 +71,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">permutation_test_plot</span><span class="op">(</span>style<span class="op">=</span><span class="st">'boxplot'</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
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diff --git a/reference/permute_sample_order.html b/reference/permute_sample_order.html
index 544e0d5..68299b9 100644
--- a/reference/permute_sample_order.html
+++ b/reference/permute_sample_order.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="The order of samples in the data matrix is randomly permuted. The relationship between the samples and the sample meta data is maintained."><title>Permute Sample Order — permute_sample_order • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Permute Sample Order — permute_sample_order"><meta property="og:description" content="The order of samples in the data matrix is randomly permuted. The relationship between the samples and the sample meta data is maintained."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Permute Sample Order — permute_sample_order • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Permute Sample Order — permute_sample_order"><meta name="description" content="The order of samples in the data matrix is randomly permuted. The relationship between the samples and the sample meta data is maintained."><meta property="og:description" content="The order of samples in the data matrix is randomly permuted. The relationship between the samples and the sample meta data is maintained."></head><body>
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     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
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+
 
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+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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-      <img src="" class="logo" alt=""><h1>Permute Sample Order</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/permute_sample_order_class.R" class="external-link"><code>R/permute_sample_order_class.R</code></a></small>
+
+      <h1>Permute Sample Order</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/permute_sample_order_class.R" class="external-link"><code>R/permute_sample_order_class.R</code></a></small>
       <div class="d-none name"><code>permute_sample_order.Rd</code></div>
     </div>
 
@@ -59,22 +47,22 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>number_of_permutations</dt>
+
+
+<dl><dt id="arg-number-of-permutations">number_of_permutations<a class="anchor" aria-label="anchor" href="#arg-number-of-permutations"></a></dt>
 <dd><p>(numeric, integer) The number of times the sample order is permuted. The default is <code>10</code>.<br></p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>permute_sample_order</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>results</code></td><td>(data.frame)</td></tr><tr><td><code>metric</code></td><td>(data.frame)</td></tr><tr><td><code>metric.train</code></td><td>(numeric)</td></tr></table></div>
+    <p>A  <code>permute_sample_order</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>results</code></td><td>(data.frame)</td></tr><tr><td><code>metric</code></td><td>(data.frame)</td></tr><tr><td><code>metric.train</code></td><td>(numeric)</td></tr></table></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>permute_sample_order</code> object inherits the following <code>struct</code> classes: <br><br><code>[permute_sample_order]</code> &gt;&gt; <code>[resampler]</code> &gt;&gt; <code>[iterator]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -87,23 +75,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">permute_sample_order</span><span class="op">(</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/pls_regcoeff_plot-1.png b/reference/pls_regcoeff_plot-1.png
index 3ffe620..1fbc530 100644
Binary files a/reference/pls_regcoeff_plot-1.png and b/reference/pls_regcoeff_plot-1.png differ
diff --git a/reference/pls_regcoeff_plot.html b/reference/pls_regcoeff_plot.html
index afeb8c0..f2cc4d3 100644
--- a/reference/pls_regcoeff_plot.html
+++ b/reference/pls_regcoeff_plot.html
@@ -1,52 +1,40 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Plots the regression coefficients of a PLSDA model.
-Plots the regression coefficient scores of a PLSDA model"><title>pls_regcoeff_plot class — pls_regcoeff_plot • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="pls_regcoeff_plot class — pls_regcoeff_plot"><meta property="og:description" content="Plots the regression coefficients of a PLSDA model.
-Plots the regression coefficient scores of a PLSDA model"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>pls_regcoeff_plot class — pls_regcoeff_plot • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="pls_regcoeff_plot class — pls_regcoeff_plot"><meta name="description" content="Plots the regression coefficients of a PLSDA model.
+Plots the regression coefficient scores of a PLSDA model"><meta property="og:description" content="Plots the regression coefficients of a PLSDA model.
+Plots the regression coefficient scores of a PLSDA model"></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary"><div class="container">
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     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.17.3</small>
+
 
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>pls_regcoeff_plot class</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/PLSDA_charts.R" class="external-link"><code>R/PLSDA_charts.R</code></a></small>
+
+      <h1>pls_regcoeff_plot class</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/PLSDA_charts.R" class="external-link"><code>R/PLSDA_charts.R</code></a></small>
       <div class="d-none name"><code>pls_regcoeff_plot.Rd</code></div>
     </div>
 
@@ -62,24 +50,22 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>ycol</dt>
+
+
+<dl><dt id="arg-ycol">ycol<a class="anchor" aria-label="anchor" href="#arg-ycol"></a></dt>
 <dd><p>(character, numeric, integer) The Y column to plot. The default is <code>1</code>.<br></p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>additional slots and values passed to struct_class</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>
+    <p>A  <code>
 pls_regcoeff_plot
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
-
-
 <p>struct object</p>
     </div>
     <div class="section level2">
@@ -89,7 +75,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 </ul></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>pls_regcoeff_plot</code> object inherits the following <code>struct</code> classes: <br><br><code>[pls_regcoeff_plot]</code> &gt;&gt; <code>[chart]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -117,23 +103,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-plt img"><img src="pls_regcoeff_plot-1.png" alt="" width="700" height="433"></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/pls_scores_plot,.html b/reference/pls_scores_plot,.html
new file mode 100644
index 0000000..dea3ab8
--- /dev/null
+++ b/reference/pls_scores_plot,.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://computational-metabolomics.github.io/structToolbox/reference/pls_scores_plot.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://computational-metabolomics.github.io/structToolbox/reference/pls_scores_plot.html">
+  </head>
+</html>
+
diff --git a/reference/pls_scores_plot-1.png b/reference/pls_scores_plot-1.png
index 56c03d4..281a131 100644
Binary files a/reference/pls_scores_plot-1.png and b/reference/pls_scores_plot-1.png differ
diff --git a/reference/pls_scores_plot.html b/reference/pls_scores_plot.html
index 5ec7424..4901025 100644
--- a/reference/pls_scores_plot.html
+++ b/reference/pls_scores_plot.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="A scatter plot of the selected PLSDA scores."><title>PLSDA scores plot — pls_scores_plot • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="PLSDA scores plot — pls_scores_plot"><meta property="og:description" content="A scatter plot of the selected PLSDA scores."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>PLSDA scores plot — pls_scores_plot • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="PLSDA scores plot — pls_scores_plot"><meta name="description" content="A scatter plot of the selected PLSDA scores."><meta property="og:description" content="A scatter plot of the selected PLSDA scores."></head><body>
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-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
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-    <a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a>
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-      </ul><form class="form-inline my-2 my-lg-0" role="search">
-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
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-      <ul class="navbar-nav"><li class="nav-item">
-  <a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="github">
-    <span class="fab fa fab fa-github fa-lg"></span>
-     
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>PLSDA scores plot</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/PLSDA_charts.R" class="external-link"><code>R/PLSDA_charts.R</code></a></small>
+
+      <h1>PLSDA scores plot</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/PLSDA_charts.R" class="external-link"><code>R/PLSDA_charts.R</code></a></small>
       <div class="d-none name"><code>pls_scores_plot.Rd</code></div>
     </div>
 
@@ -87,26 +75,28 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>xcol</dt>
+
+
+<dl><dt id="arg-xcol">xcol<a class="anchor" aria-label="anchor" href="#arg-xcol"></a></dt>
 <dd><p>(numeric, integer, character) The column name, or index, of data to plot on the x-axis. The default is <code>"LV1"</code>.</p></dd>
 
 
-<dt>ycol</dt>
+<dt id="arg-ycol">ycol<a class="anchor" aria-label="anchor" href="#arg-ycol"></a></dt>
 <dd><p>(numeric, integer, character) The column name, or index, of data to plot on the y-axis. The default is <code>"LV2"</code>.</p></dd>
 
 
-<dt>points_to_label</dt>
+<dt id="arg-points-to-label">points_to_label<a class="anchor" aria-label="anchor" href="#arg-points-to-label"></a></dt>
 <dd><p>(character) Points to label. Allowed values are limited to the following:</p><ul><li><p><code>"none"</code>: No samples labels are displayed.</p></li>
 <li><p><code>"all"</code>: The labels for all samples are displayed.</p></li>
 <li><p><code>"outliers"</code>: Labels for for potential outlier samples are displayed.</p></li>
 </ul><p>The default is <code>"none"</code>.</p></dd>
 
 
-<dt>factor_name</dt>
+<dt id="arg-factor-name">factor_name<a class="anchor" aria-label="anchor" href="#arg-factor-name"></a></dt>
 <dd><p>(character) The name of a sample-meta column to use.</p></dd>
 
 
-<dt>ellipse</dt>
+<dt id="arg-ellipse">ellipse<a class="anchor" aria-label="anchor" href="#arg-ellipse"></a></dt>
 <dd><p>(character) Plot ellipses. Allowed values are limited to the following:</p><ul><li><p><code>"all"</code>: Ellipses are plotted for all groups and all samples.</p></li>
 <li><p><code>"group"</code>: Ellipses are plotted for all groups.</p></li>
 <li><p><code>"none"</code>: Ellipses are not included on the plot.</p></li>
@@ -114,48 +104,46 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </ul><p>The default is <code>"all"</code>.</p></dd>
 
 
-<dt>ellipse_type</dt>
+<dt id="arg-ellipse-type">ellipse_type<a class="anchor" aria-label="anchor" href="#arg-ellipse-type"></a></dt>
 <dd><p>(character) Type of ellipse. Allowed values are limited to the following:</p><ul><li><p><code>"norm"</code>: Multivariate normal (p = 0.95).</p></li>
 <li><p><code>"t"</code>: Multivariate t (p = 0.95).</p></li>
 </ul><p>The default is <code>"norm"</code>.</p></dd>
 
 
-<dt>ellipse_confidence</dt>
+<dt id="arg-ellipse-confidence">ellipse_confidence<a class="anchor" aria-label="anchor" href="#arg-ellipse-confidence"></a></dt>
 <dd><p>(numeric) The confidence level for plotting ellipses. The default is <code>0.95</code>.<br></p></dd>
 
 
-<dt>label_filter</dt>
+<dt id="arg-label-filter">label_filter<a class="anchor" aria-label="anchor" href="#arg-label-filter"></a></dt>
 <dd><p>(character) Labels are only plotted for the named groups. If zero-length then all groups are included. The default is <code>character(0)</code>.</p></dd>
 
 
-<dt>label_factor</dt>
+<dt id="arg-label-factor">label_factor<a class="anchor" aria-label="anchor" href="#arg-label-factor"></a></dt>
 <dd><p>(character) The column name of sample_meta to use for labelling samples on the plot. "rownames" will use the row names from sample_meta. The default is <code>"rownames"</code>.</p></dd>
 
 
-<dt>label_size</dt>
+<dt id="arg-label-size">label_size<a class="anchor" aria-label="anchor" href="#arg-label-size"></a></dt>
 <dd><p>(numeric) The text size of labels. Note this is not in Font Units. The default is <code>3.88</code>.<br></p></dd>
 
 
-<dt>components</dt>
+<dt id="arg-components">components<a class="anchor" aria-label="anchor" href="#arg-components"></a></dt>
 <dd><p>(numeric, integer, NULL) The principal components used to generate the plot. If provided this parameter overrides xcol and ycol params. The default is <code>NULL</code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>
+    <p>A  <code>
 pls_scores_plot
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>pls_scores_plot</code> object inherits the following <code>struct</code> classes: <br><br><code>[pls_scores_plot]</code> &gt;&gt; <code>[scatter_chart]</code> &gt;&gt; <code>[chart]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -184,23 +172,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-plt img"><img src="pls_scores_plot-1.png" alt="" width="700" height="433"></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
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+
 
   </body></html>
 
diff --git a/reference/pls_vip_plot-1.png b/reference/pls_vip_plot-1.png
index dccdc68..4a811a9 100644
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diff --git a/reference/pls_vip_plot.html b/reference/pls_vip_plot.html
index caf182e..67d33bd 100644
--- a/reference/pls_vip_plot.html
+++ b/reference/pls_vip_plot.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="A plot of the Variable Importance for Projection (VIP) scores for a PLSDA model."><title>PLSDA VIP plot — pls_vip_plot • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="PLSDA VIP plot — pls_vip_plot"><meta property="og:description" content="A plot of the Variable Importance for Projection (VIP) scores for a PLSDA model."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>PLSDA VIP plot — pls_vip_plot • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="PLSDA VIP plot — pls_vip_plot"><meta name="description" content="A plot of the Variable Importance for Projection (VIP) scores for a PLSDA model."><meta property="og:description" content="A plot of the Variable Importance for Projection (VIP) scores for a PLSDA model."></head><body>
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+
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.17.3</small>
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>PLSDA VIP plot</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/PLSDA_charts.R" class="external-link"><code>R/PLSDA_charts.R</code></a></small>
+
+      <h1>PLSDA VIP plot</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/PLSDA_charts.R" class="external-link"><code>R/PLSDA_charts.R</code></a></small>
       <div class="d-none name"><code>pls_vip_plot.Rd</code></div>
     </div>
 
@@ -59,23 +47,23 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>threshold</dt>
+
+
+<dl><dt id="arg-threshold">threshold<a class="anchor" aria-label="anchor" href="#arg-threshold"></a></dt>
 <dd><p>(numeric, integer) The threshold for indicating significant features. The default is <code>1</code>.<br></p></dd>
 
 
-<dt>ycol</dt>
+<dt id="arg-ycol">ycol<a class="anchor" aria-label="anchor" href="#arg-ycol"></a></dt>
 <dd><p>(character, numeric, integer) The column of the Y block to be plotted. The default is <code>1</code>.<br></p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>
+    <p>A  <code>
 pls_vip_plot
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
@@ -87,7 +75,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 </ul></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>pls_vip_plot</code> object inherits the following <code>struct</code> classes: <br><br><code>[pls_vip_plot]</code> &gt;&gt; <code>[chart]</code> &gt;&gt; <code>[stato]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -116,23 +104,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-plt img"><img src="pls_vip_plot-1.png" alt="" width="700" height="433"></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
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diff --git a/reference/plsda_feature_importance_plot-1.png b/reference/plsda_feature_importance_plot-1.png
index 91a070c..cbf6c37 100644
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diff --git a/reference/plsda_feature_importance_plot.html b/reference/plsda_feature_importance_plot.html
index 9b39126..dc77d7c 100644
--- a/reference/plsda_feature_importance_plot.html
+++ b/reference/plsda_feature_importance_plot.html
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-      <img src="" class="logo" alt=""><h1>PLSDA feature importance summary plot</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/plsda_feature_significance_chart.R" class="external-link"><code>R/plsda_feature_significance_chart.R</code></a></small>
+
+      <h1>PLSDA feature importance summary plot</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/plsda_feature_significance_chart.R" class="external-link"><code>R/plsda_feature_significance_chart.R</code></a></small>
       <div class="d-none name"><code>plsda_feature_importance_plot.Rd</code></div>
     </div>
 
@@ -59,26 +47,26 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>n_features</dt>
+
+
+<dl><dt id="arg-n-features">n_features<a class="anchor" aria-label="anchor" href="#arg-n-features"></a></dt>
 <dd><p>(numeric, integer) The number of features to include in the summary. The default is <code>30</code>.<br></p></dd>
 
 
-<dt>metric</dt>
+<dt id="arg-metric">metric<a class="anchor" aria-label="anchor" href="#arg-metric"></a></dt>
 <dd><p>(character) Metric to plot. Allowed values are limited to the following:</p><ul><li><p><code>"sr"</code>: Plot Selectivity Ratio.</p></li>
 <li><p><code>"sr_pvalue"</code>: Plot SR p-values.</p></li>
 <li><p><code>"vip"</code>: Plot Variable Importance in Projection scores.</p></li>
 </ul><p>The default is <code>"vip"</code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>
+    <p>A  <code>
 plsda_feature_importance_plot
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
@@ -92,7 +80,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 </ul></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>plsda_feature_importance_plot</code> object inherits the following <code>struct</code> classes: <br><br><code>[plsda_feature_importance_plot]</code> &gt;&gt; <code>[chart]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -107,8 +95,8 @@ <h2 id="references">References<a class="anchor" aria-label="anchor" href="#refer
 <p>Wickham H (2007). "Reshaping Data with the reshape Package." <em>Journal
 of Statistical Software</em>, <em>21</em>(12), 1-20.
 <a href="http://www.jstatsoft.org/v21/i12/" class="external-link">http://www.jstatsoft.org/v21/i12/</a>.</p>
-<p>Wilke C (2023). <em>cowplot: Streamlined Plot Theme and Plot Annotations
-for 'ggplot2'</em>. R package version 1.1.2,
+<p>Wilke C (2024). <em>cowplot: Streamlined Plot Theme and Plot Annotations
+for 'ggplot2'</em>. R package version 1.1.3,
 <a href="https://CRAN.R-project.org/package=cowplot" class="external-link">https://CRAN.R-project.org/package=cowplot</a>.</p>
     </div>
 
@@ -127,23 +115,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-plt img"><img src="plsda_feature_importance_plot-1.png" alt="" width="700" height="433"></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
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-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
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diff --git a/reference/plsda_predicted_plot-1.png b/reference/plsda_predicted_plot-1.png
index a7a6b11..ac77d42 100644
Binary files a/reference/plsda_predicted_plot-1.png and b/reference/plsda_predicted_plot-1.png differ
diff --git a/reference/plsda_predicted_plot.html b/reference/plsda_predicted_plot.html
index 18a68bc..750ed34 100644
--- a/reference/plsda_predicted_plot.html
+++ b/reference/plsda_predicted_plot.html
@@ -1,50 +1,38 @@
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>PLSDA predicted plot</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/PLSDA_charts.R" class="external-link"><code>R/PLSDA_charts.R</code></a></small>
+
+      <h1>PLSDA predicted plot</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/PLSDA_charts.R" class="external-link"><code>R/PLSDA_charts.R</code></a></small>
       <div class="d-none name"><code>plsda_predicted_plot.Rd</code></div>
     </div>
 
@@ -59,30 +47,30 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>factor_name</dt>
+
+
+<dl><dt id="arg-factor-name">factor_name<a class="anchor" aria-label="anchor" href="#arg-factor-name"></a></dt>
 <dd><p>(character) The name of a sample-meta column to use.</p></dd>
 
 
-<dt>style</dt>
+<dt id="arg-style">style<a class="anchor" aria-label="anchor" href="#arg-style"></a></dt>
 <dd><p>(character) Plot style. Allowed values are limited to the following:</p><ul><li><p><code>"boxplot"</code>: A boxplot.</p></li>
 <li><p><code>"violin"</code>: A violin plot.</p></li>
 <li><p><code>"density"</code>: A density plot.</p></li>
 </ul><p>The default is <code>"boxplot"</code>.</p></dd>
 
 
-<dt>ycol</dt>
+<dt id="arg-ycol">ycol<a class="anchor" aria-label="anchor" href="#arg-ycol"></a></dt>
 <dd><p>(character, numeric, integer) The column of the Y block to be plotted. The default is <code>1</code>.<br></p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>
+    <p>A  <code>
 plsda_predicted_plot
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
@@ -94,7 +82,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 </ul></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>plsda_predicted_plot</code> object inherits the following <code>struct</code> classes: <br><br><code>[plsda_predicted_plot]</code> &gt;&gt; <code>[chart]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -124,23 +112,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-plt img"><img src="plsda_predicted_plot-1.png" alt="" width="700" height="433"></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/plsda_roc_plot-1.png b/reference/plsda_roc_plot-1.png
index 1f628fa..fdbf97b 100644
Binary files a/reference/plsda_roc_plot-1.png and b/reference/plsda_roc_plot-1.png differ
diff --git a/reference/plsda_roc_plot.html b/reference/plsda_roc_plot.html
index 3332056..70866cd 100644
--- a/reference/plsda_roc_plot.html
+++ b/reference/plsda_roc_plot.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="A Receiver Operator Characteristic (ROC) plot for PLSDA models computed by adjusting the threshold for assigning group labels from PLS predictions."><title>PLSDA ROC plot — plsda_roc_plot • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="PLSDA ROC plot — plsda_roc_plot"><meta property="og:description" content="A Receiver Operator Characteristic (ROC) plot for PLSDA models computed by adjusting the threshold for assigning group labels from PLS predictions."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>PLSDA ROC plot — plsda_roc_plot • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="PLSDA ROC plot — plsda_roc_plot"><meta name="description" content="A Receiver Operator Characteristic (ROC) plot for PLSDA models computed by adjusting the threshold for assigning group labels from PLS predictions."><meta property="og:description" content="A Receiver Operator Characteristic (ROC) plot for PLSDA models computed by adjusting the threshold for assigning group labels from PLS predictions."></head><body>
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     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
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+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.17.3</small>
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
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+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
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+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>PLSDA ROC plot</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/PLSDA_charts.R" class="external-link"><code>R/PLSDA_charts.R</code></a></small>
+
+      <h1>PLSDA ROC plot</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/PLSDA_charts.R" class="external-link"><code>R/PLSDA_charts.R</code></a></small>
       <div class="d-none name"><code>plsda_roc_plot.Rd</code></div>
     </div>
 
@@ -59,23 +47,23 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>factor_name</dt>
+
+
+<dl><dt id="arg-factor-name">factor_name<a class="anchor" aria-label="anchor" href="#arg-factor-name"></a></dt>
 <dd><p>(character) The name of a sample-meta column to use.</p></dd>
 
 
-<dt>ycol</dt>
+<dt id="arg-ycol">ycol<a class="anchor" aria-label="anchor" href="#arg-ycol"></a></dt>
 <dd><p>(character, numeric, integer) The column of the Y block to be plotted. The default is <code>1</code>.<br></p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>
+    <p>A  <code>
 plsda_roc_plot
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
@@ -87,7 +75,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 </ul></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>plsda_roc_plot</code> object inherits the following <code>struct</code> classes: <br><br><code>[plsda_roc_plot]</code> &gt;&gt; <code>[chart]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -116,23 +104,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-plt img"><img src="plsda_roc_plot-1.png" alt="" width="700" height="433"></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/plsda_scores_plot.html b/reference/plsda_scores_plot.html
new file mode 100644
index 0000000..dea3ab8
--- /dev/null
+++ b/reference/plsda_scores_plot.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://computational-metabolomics.github.io/structToolbox/reference/pls_scores_plot.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://computational-metabolomics.github.io/structToolbox/reference/pls_scores_plot.html">
+  </head>
+</html>
+
diff --git a/reference/plsr_cook_dist.html b/reference/plsr_cook_dist.html
index 7fa53d8..f1487aa 100644
--- a/reference/plsr_cook_dist.html
+++ b/reference/plsr_cook_dist.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="A barchart of Cook's distance for each sample used to train a PLSR model. Cook's distance is used to estimate the influence of a sample on the model and can be used to identify potential outliers."><title>Cook's distance barchart — plsr_cook_dist • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Cook's distance barchart — plsr_cook_dist"><meta property="og:description" content="A barchart of Cook's distance for each sample used to train a PLSR model. Cook's distance is used to estimate the influence of a sample on the model and can be used to identify potential outliers."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Cook's distance barchart — plsr_cook_dist • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Cook's distance barchart — plsr_cook_dist"><meta name="description" content="A barchart of Cook's distance for each sample used to train a PLSR model. Cook's distance is used to estimate the influence of a sample on the model and can be used to identify potential outliers."><meta property="og:description" content="A barchart of Cook's distance for each sample used to train a PLSR model. Cook's distance is used to estimate the influence of a sample on the model and can be used to identify potential outliers."></head><body>
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     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
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+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Cook's distance barchart</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/PLSR_class.R" class="external-link"><code>R/PLSR_class.R</code></a></small>
+
+      <h1>Cook's distance barchart</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/PLSR_class.R" class="external-link"><code>R/PLSR_class.R</code></a></small>
       <div class="d-none name"><code>plsr_cook_dist.Rd</code></div>
     </div>
 
@@ -59,26 +47,26 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>ycol</dt>
+
+
+<dl><dt id="arg-ycol">ycol<a class="anchor" aria-label="anchor" href="#arg-ycol"></a></dt>
 <dd><p>(numeric, integer, character) The y-block column to plot. The default is <code>1</code>.<br></p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>
+    <p>A  <code>
 plsr_cook_dist
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>plsr_cook_dist</code> object inherits the following <code>struct</code> classes: <br><br><code>[plsr_cook_dist]</code> &gt;&gt; <code>[chart]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -91,23 +79,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">plsr_cook_dist</span><span class="op">(</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
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diff --git a/reference/plsr_prediction_plot.html b/reference/plsr_prediction_plot.html
index ccfc571..a9c619e 100644
--- a/reference/plsr_prediction_plot.html
+++ b/reference/plsr_prediction_plot.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="A scatter plot of the true response values against the predicted values for a PLSR model."><title>PLSR prediction plot — plsr_prediction_plot • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="PLSR prediction plot — plsr_prediction_plot"><meta property="og:description" content="A scatter plot of the true response values against the predicted values for a PLSR model."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>PLSR prediction plot — plsr_prediction_plot • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="PLSR prediction plot — plsr_prediction_plot"><meta name="description" content="A scatter plot of the true response values against the predicted values for a PLSR model."><meta property="og:description" content="A scatter plot of the true response values against the predicted values for a PLSR model."></head><body>
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     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
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+
 
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+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>PLSR prediction plot</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/PLSR_class.R" class="external-link"><code>R/PLSR_class.R</code></a></small>
+
+      <h1>PLSR prediction plot</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/PLSR_class.R" class="external-link"><code>R/PLSR_class.R</code></a></small>
       <div class="d-none name"><code>plsr_prediction_plot.Rd</code></div>
     </div>
 
@@ -59,26 +47,26 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>ycol</dt>
+
+
+<dl><dt id="arg-ycol">ycol<a class="anchor" aria-label="anchor" href="#arg-ycol"></a></dt>
 <dd><p>(numeric, integer, character) The y-block column to plot. The default is <code>1</code>.<br></p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>
+    <p>A  <code>
 plsr_prediction_plot
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>plsr_prediction_plot</code> object inherits the following <code>struct</code> classes: <br><br><code>[plsr_prediction_plot]</code> &gt;&gt; <code>[chart]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -91,23 +79,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">plsr_prediction_plot</span><span class="op">(</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
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diff --git a/reference/plsr_qq_plot.html b/reference/plsr_qq_plot.html
index 3059a5d..bc9ba2d 100644
--- a/reference/plsr_qq_plot.html
+++ b/reference/plsr_qq_plot.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="A plot of the quantiles of the residuals from a PLSR model against the quantiles of a normal distribution."><title>PLSR QQ plot — plsr_qq_plot • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="PLSR QQ plot — plsr_qq_plot"><meta property="og:description" content="A plot of the quantiles of the residuals from a PLSR model against the quantiles of a normal distribution."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>PLSR QQ plot — plsr_qq_plot • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="PLSR QQ plot — plsr_qq_plot"><meta name="description" content="A plot of the quantiles of the residuals from a PLSR model against the quantiles of a normal distribution."><meta property="og:description" content="A plot of the quantiles of the residuals from a PLSR model against the quantiles of a normal distribution."></head><body>
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-    
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary"><div class="container">
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+
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.17.3</small>
+
 
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>PLSR QQ plot</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/PLSR_class.R" class="external-link"><code>R/PLSR_class.R</code></a></small>
+
+      <h1>PLSR QQ plot</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/PLSR_class.R" class="external-link"><code>R/PLSR_class.R</code></a></small>
       <div class="d-none name"><code>plsr_qq_plot.Rd</code></div>
     </div>
 
@@ -59,26 +47,26 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>ycol</dt>
+
+
+<dl><dt id="arg-ycol">ycol<a class="anchor" aria-label="anchor" href="#arg-ycol"></a></dt>
 <dd><p>(numeric, integer, character) The y-block column to plot. The default is <code>1</code>.<br></p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>
+    <p>A  <code>
 plsr_qq_plot
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>plsr_qq_plot</code> object inherits the following <code>struct</code> classes: <br><br><code>[plsr_qq_plot]</code> &gt;&gt; <code>[chart]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -91,23 +79,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">plsr_qq_plot</span><span class="op">(</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/plsr_residual_hist.html b/reference/plsr_residual_hist.html
index 0b5e368..1e7522f 100644
--- a/reference/plsr_residual_hist.html
+++ b/reference/plsr_residual_hist.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="A histogram of the residuals for a PLSR model."><title>PLSR residuals histogram — plsr_residual_hist • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="PLSR residuals histogram — plsr_residual_hist"><meta property="og:description" content="A histogram of the residuals for a PLSR model."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>PLSR residuals histogram — plsr_residual_hist • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="PLSR residuals histogram — plsr_residual_hist"><meta name="description" content="A histogram of the residuals for a PLSR model."><meta property="og:description" content="A histogram of the residuals for a PLSR model."></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary"><div class="container">
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+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.17.3</small>
+
 
-    
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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-      <ul class="navbar-nav me-auto"><li class="active nav-item">
-  <a class="nav-link" href="../reference/index.html">Reference</a>
-</li>
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
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-    <a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a>
-  </div>
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-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>PLSR residuals histogram</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/PLSR_class.R" class="external-link"><code>R/PLSR_class.R</code></a></small>
+
+      <h1>PLSR residuals histogram</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/PLSR_class.R" class="external-link"><code>R/PLSR_class.R</code></a></small>
       <div class="d-none name"><code>plsr_residual_hist.Rd</code></div>
     </div>
 
@@ -59,26 +47,26 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>ycol</dt>
+
+
+<dl><dt id="arg-ycol">ycol<a class="anchor" aria-label="anchor" href="#arg-ycol"></a></dt>
 <dd><p>(numeric, integer, character) The y-block column to plot. The default is <code>1</code>.<br></p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>
+    <p>A  <code>
 plsr_residual_hist
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>plsr_residual_hist</code> object inherits the following <code>struct</code> classes: <br><br><code>[plsr_residual_hist]</code> &gt;&gt; <code>[chart]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -91,23 +79,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">plsr_residual_hist</span><span class="op">(</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
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diff --git a/reference/pqn_norm.html b/reference/pqn_norm.html
index fa0a5da..c90526d 100644
--- a/reference/pqn_norm.html
+++ b/reference/pqn_norm.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="PQN is used to normalise for differences in concentration between samples. It makes use of Quality Control (QC) samples as a reference. PQN scales by the median change relative to the reference in order to be more robust against changes caused by response to perturbation."><title>Probabilistic Quotient Normalisation (PQN) — pqn_norm • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Probabilistic Quotient Normalisation (PQN) — pqn_norm"><meta property="og:description" content="PQN is used to normalise for differences in concentration between samples. It makes use of Quality Control (QC) samples as a reference. PQN scales by the median change relative to the reference in order to be more robust against changes caused by response to perturbation."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Probabilistic Quotient Normalisation (PQN) — pqn_norm • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Probabilistic Quotient Normalisation (PQN) — pqn_norm"><meta name="description" content="PQN is used to normalise for differences in concentration between samples. It makes use of Quality Control (QC) samples as a reference. PQN scales by the median change relative to the reference in order to be more robust against changes caused by response to perturbation."><meta property="og:description" content="PQN is used to normalise for differences in concentration between samples. It makes use of Quality Control (QC) samples as a reference. PQN scales by the median change relative to the reference in order to be more robust against changes caused by response to perturbation."></head><body>
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+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.17.3</small>
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
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-      <img src="" class="logo" alt=""><h1>Probabilistic Quotient Normalisation (PQN)</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/pqn_norm_method_class.R" class="external-link"><code>R/pqn_norm_method_class.R</code></a></small>
+
+      <h1>Probabilistic Quotient Normalisation (PQN)</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/pqn_norm_method_class.R" class="external-link"><code>R/pqn_norm_method_class.R</code></a></small>
       <div class="d-none name"><code>pqn_norm.Rd</code></div>
     </div>
 
@@ -67,57 +55,56 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>qc_label</dt>
+
+
+<dl><dt id="arg-qc-label">qc_label<a class="anchor" aria-label="anchor" href="#arg-qc-label"></a></dt>
 <dd><p>(character) The label used to identify QC samples. The default is <code>"QC"</code>.</p></dd>
 
 
-<dt>factor_name</dt>
+<dt id="arg-factor-name">factor_name<a class="anchor" aria-label="anchor" href="#arg-factor-name"></a></dt>
 <dd><p>(character) The name of a sample-meta column to use.</p></dd>
 
 
-<dt>qc_frac</dt>
+<dt id="arg-qc-frac">qc_frac<a class="anchor" aria-label="anchor" href="#arg-qc-frac"></a></dt>
 <dd><p>(numeric) A value between 0 and 1 to indicate the minimum proportion of QC samples a feature must be present in for it to be included when computing the reference. Default qc_frac = 0. . The default is <code>0</code>.<br></p></dd>
 
 
-<dt>sample_frac</dt>
+<dt id="arg-sample-frac">sample_frac<a class="anchor" aria-label="anchor" href="#arg-sample-frac"></a></dt>
 <dd><p>(numeric) A value between 0 and 1 to indicate the minimum proportion of samples a feature must be present in for it to be considered when computing the normalisation coefficients. . The default is <code>0</code>.<br></p></dd>
 
 
-<dt>ref_method</dt>
+<dt id="arg-ref-method">ref_method<a class="anchor" aria-label="anchor" href="#arg-ref-method"></a></dt>
 <dd><p>(character) Reference computation method. Allowed values are limited to the following:</p><ul><li><p><code>"mean"</code>: The reference is computed as the mean of the samples matching the qc_label input.</p></li>
 <li><p><code>"median"</code>: The reference is computed as the median of the samples matching the qc_label_input.</p></li>
 </ul><p>The default is <code>"mean"</code>.</p></dd>
 
 
-<dt>ref_mean</dt>
+<dt id="arg-ref-mean">ref_mean<a class="anchor" aria-label="anchor" href="#arg-ref-mean"></a></dt>
 <dd><p>(numeric, NULL) A single sample to use as the reference for normalisation. If set to NULL then the reference will be computed based on the other input parameters (ref_mean, qc_label etc). . The default is <code>NULL</code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>pqn_norm</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>normalised</code></td><td>(DatasetExperiment) A DatasetExperiment object containing the normalised data.</td></tr><tr><td><code>coeff</code></td><td>(data.frame) The normalisation coefficients calculated by PQN.</td></tr></table></div>
+    <p>A  <code>pqn_norm</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>normalised</code></td><td>(DatasetExperiment) A DatasetExperiment object containing the normalised data.</td></tr><tr><td><code>coeff</code></td><td>(data.frame) The normalisation coefficients calculated by PQN.</td></tr></table></div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>pmp</code></p></li>
 </ul></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>pqn_norm</code> object inherits the following <code>struct</code> classes: <br><br><code>[pqn_norm]</code> &gt;&gt; <code>[model]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
     <div class="section level2">
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
-    <p>Jankevics A, Lloyd GR, Weber RJM (2023). <em>pmp: Peak Matrix Processing
-and signal batch correction for metabolomics datasets</em>.
-doi:10.18129/B9.bioc.pmp <a href="https://doi.org/10.18129/B9.bioc.pmp" class="external-link">https://doi.org/10.18129/B9.bioc.pmp</a>, R
-package version 1.14.0, <a href="https://bioconductor.org/packages/pmp" class="external-link">https://bioconductor.org/packages/pmp</a>.</p>
+    <p>Jankevics A, Lloyd GR, Weber RJM (????). <em>pmp: Peak Matrix Processing
+and signal batch correction for metabolomics datasets</em>. R package
+version 1.15.1.</p>
     </div>
 
     <div class="section level2">
@@ -136,23 +123,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/pqn_norm_hist.html b/reference/pqn_norm_hist.html
index cc2b776..e89cfd4 100644
--- a/reference/pqn_norm_hist.html
+++ b/reference/pqn_norm_hist.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="A histogram of the PQN coefficients for all features"><title>PQN coefficient histogram — pqn_norm_hist • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="PQN coefficient histogram — pqn_norm_hist"><meta property="og:description" content="A histogram of the PQN coefficients for all features"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>PQN coefficient histogram — pqn_norm_hist • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="PQN coefficient histogram — pqn_norm_hist"><meta name="description" content="A histogram of the PQN coefficients for all features"><meta property="og:description" content="A histogram of the PQN coefficients for all features"></head><body>
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 <li class="nav-item dropdown">
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+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>PQN coefficient histogram</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/pqn_norm_method_class.R" class="external-link"><code>R/pqn_norm_method_class.R</code></a></small>
+
+      <h1>PQN coefficient histogram</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/pqn_norm_method_class.R" class="external-link"><code>R/pqn_norm_method_class.R</code></a></small>
       <div class="d-none name"><code>pqn_norm_hist.Rd</code></div>
     </div>
 
@@ -59,22 +47,22 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>...</dt>
+
+
+<dl><dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>
+    <p>A  <code>
 pqn_norm_hist
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>pqn_norm_hist</code> object inherits the following <code>struct</code> classes: <br><br><code>[pqn_norm_hist]</code> &gt;&gt; <code>[chart]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -86,23 +74,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">pqn_norm_hist</span><span class="op">(</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
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diff --git a/reference/prop_na.html b/reference/prop_na.html
index c13f4b6..f36595e 100644
--- a/reference/prop_na.html
+++ b/reference/prop_na.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="A Fisher's exact test is used to compare the number of missing values in each group. Multiple test corrected p-values are computed to indicate whether there is a significant difference in the number of missing values across groups for each feature."><title>Fisher's exact test for missing values — prop_na • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fisher's exact test for missing values — prop_na"><meta property="og:description" content="A Fisher's exact test is used to compare the number of missing values in each group. Multiple test corrected p-values are computed to indicate whether there is a significant difference in the number of missing values across groups for each feature."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Fisher's exact test for missing values — prop_na • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fisher's exact test for missing values — prop_na"><meta name="description" content="A Fisher's exact test is used to compare the number of missing values in each group. Multiple test corrected p-values are computed to indicate whether there is a significant difference in the number of missing values across groups for each feature."><meta property="og:description" content="A Fisher's exact test is used to compare the number of missing values in each group. Multiple test corrected p-values are computed to indicate whether there is a significant difference in the number of missing values across groups for each feature."></head><body>
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     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
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 <li class="nav-item dropdown">
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+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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-    
+
   </div>
 </nav><div class="container template-reference-topic">
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Fisher's exact test for missing values</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/prop_na_class.R" class="external-link"><code>R/prop_na_class.R</code></a></small>
+
+      <h1>Fisher's exact test for missing values</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/prop_na_class.R" class="external-link"><code>R/prop_na_class.R</code></a></small>
       <div class="d-none name"><code>prop_na.Rd</code></div>
     </div>
 
@@ -59,34 +47,34 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>alpha</dt>
+
+
+<dl><dt id="arg-alpha">alpha<a class="anchor" aria-label="anchor" href="#arg-alpha"></a></dt>
 <dd><p>(numeric) The p-value cutoff for determining significance. The default is <code>0.05</code>.<br></p></dd>
 
 
-<dt>mtc</dt>
+<dt id="arg-mtc">mtc<a class="anchor" aria-label="anchor" href="#arg-mtc"></a></dt>
 <dd><p>(character) Multiple test correction method. Allowed values are limited to the following:</p><ul><li><p><code>"bonferroni"</code>: Bonferroni correction in which the p-values are multiplied by the number of comparisons.</p></li>
 <li><p><code>"fdr"</code>: Benjamini and Hochberg False Discovery Rate correction.</p></li>
 <li><p><code>"none"</code>: No correction.</p></li>
 </ul><p>The default is <code>"fdr"</code>.</p></dd>
 
 
-<dt>factor_name</dt>
+<dt id="arg-factor-name">factor_name<a class="anchor" aria-label="anchor" href="#arg-factor-name"></a></dt>
 <dd><p>(character) The name of a sample-meta column to use.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>prop_na</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>p_value</code></td><td>(data.frame) The probability of observing the calculated statistic.</td></tr><tr><td><code>significant</code></td><td>(data.frame) TRUE if the calculated p-value is less than the supplied threshold (alpha).</td></tr><tr><td><code>na_count</code></td><td>(data.frame) The number of NA values per group of the chosen factor.</td></tr></table><p>struct object</p>
+    <p>A  <code>prop_na</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>p_value</code></td><td>(data.frame) The probability of observing the calculated statistic.</td></tr><tr><td><code>significant</code></td><td>(data.frame) TRUE if the calculated p-value is less than the supplied threshold (alpha).</td></tr><tr><td><code>na_count</code></td><td>(data.frame) The number of NA values per group of the chosen factor.</td></tr></table><p>struct object</p>
     </div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>prop_na</code> object inherits the following <code>struct</code> classes: <br><br><code>[prop_na]</code> &gt;&gt; <code>[model]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -102,23 +90,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
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diff --git a/reference/r_squared.html b/reference/r_squared.html
index af45ff9..72bc2e0 100644
--- a/reference/r_squared.html
+++ b/reference/r_squared.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Coefficient of determination (R-squared)</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/r_squared_class.R" class="external-link"><code>R/r_squared_class.R</code></a></small>
+
+      <h1>Coefficient of determination (R-squared)</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/r_squared_class.R" class="external-link"><code>R/r_squared_class.R</code></a></small>
       <div class="d-none name"><code>r_squared.Rd</code></div>
     </div>
 
@@ -59,21 +47,21 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>...</dt>
+
+
+<dl><dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>
+    <p>A  <code>
 r_squared
 </code> object. This object has no <code>output</code> slots.</p>
     </div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>r_squared</code> object inherits the following <code>struct</code> classes: <br><br><code>[r_squared]</code> &gt;&gt; <code>[metric]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -86,23 +74,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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diff --git a/reference/resample.html b/reference/resample.html
index 62ad164..707b4c5 100644
--- a/reference/resample.html
+++ b/reference/resample.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="New training sets are generated from the original data by selecting samples at random. This can be based on levels in a factor or on the whole dataset."><title>Data resampling — resample • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Data resampling — resample"><meta property="og:description" content="New training sets are generated from the original data by selecting samples at random. This can be based on levels in a factor or on the whole dataset."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
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-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Data resampling — resample • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Data resampling — resample"><meta name="description" content="New training sets are generated from the original data by selecting samples at random. This can be based on levels in a factor or on the whole dataset."><meta property="og:description" content="New training sets are generated from the original data by selecting samples at random. This can be based on levels in a factor or on the whole dataset."></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
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     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.17.3</small>
+
 
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-      <ul class="navbar-nav me-auto"><li class="active nav-item">
-  <a class="nav-link" href="../reference/index.html">Reference</a>
-</li>
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
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+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Data resampling</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/resample_class.R" class="external-link"><code>R/resample_class.R</code></a></small>
+
+      <h1>Data resampling</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/resample_class.R" class="external-link"><code>R/resample_class.R</code></a></small>
       <div class="d-none name"><code>resample.Rd</code></div>
     </div>
 
@@ -66,41 +54,41 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>number_of_iterations</dt>
+
+
+<dl><dt id="arg-number-of-iterations">number_of_iterations<a class="anchor" aria-label="anchor" href="#arg-number-of-iterations"></a></dt>
 <dd><p>(numeric, integer) The number of training sets to generate. The default is <code>10</code>.<br></p></dd>
 
 
-<dt>method</dt>
+<dt id="arg-method">method<a class="anchor" aria-label="anchor" href="#arg-method"></a></dt>
 <dd><p>(character) Resampling method. Allowed values are limited to the following:</p><ul><li><p><code>"split_data"</code>: Samples for the training set are selected at random from the full dataset.</p></li>
 <li><p><code>"stratified_split"</code>: Samples for the training set are randomly selected from each level of the chosen factor.</p></li>
 <li><p><code>"equal_split"</code>: Samples for the training set are selected at random from each level of the main factor such that all group sizes are equal.</p></li>
 </ul><p>The default is <code>"split_data"</code>.</p></dd>
 
 
-<dt>factor_name</dt>
+<dt id="arg-factor-name">factor_name<a class="anchor" aria-label="anchor" href="#arg-factor-name"></a></dt>
 <dd><p>(character) The name of a sample-meta column to use.</p></dd>
 
 
-<dt>p_train</dt>
+<dt id="arg-p-train">p_train<a class="anchor" aria-label="anchor" href="#arg-p-train"></a></dt>
 <dd><p>(numeric) The proportion of samples selected for the training set. The default is <code>0.8</code>.<br></p></dd>
 
 
-<dt>collect</dt>
+<dt id="arg-collect">collect<a class="anchor" aria-label="anchor" href="#arg-collect"></a></dt>
 <dd><p>(NULL, character) The name of a model output to collect over all bootstrap repetitions, in addition to the input metric. The default is <code>NULL</code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>resample</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>results.training</code></td><td>(data.frame)</td></tr><tr><td><code>results.testing</code></td><td>(data.frame)</td></tr><tr><td><code>metric</code></td><td>(data.frame)</td></tr><tr><td><code>collected</code></td><td>(list)</td></tr><tr><td><code>metric.train</code></td><td>(numeric)</td></tr><tr><td><code>metric.test</code></td><td>(numeric)</td></tr></table></div>
+    <p>A  <code>resample</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>results.training</code></td><td>(data.frame)</td></tr><tr><td><code>results.testing</code></td><td>(data.frame)</td></tr><tr><td><code>metric</code></td><td>(data.frame)</td></tr><tr><td><code>collected</code></td><td>(list)</td></tr><tr><td><code>metric.train</code></td><td>(numeric)</td></tr><tr><td><code>metric.test</code></td><td>(numeric)</td></tr></table></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>resample</code> object inherits the following <code>struct</code> classes: <br><br><code>[resample]</code> &gt;&gt; <code>[resampler]</code> &gt;&gt; <code>[iterator]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -121,23 +109,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span>    p_train <span class="op">=</span> <span class="fl">0.8</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
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-  
 
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diff --git a/reference/resample_chart.html b/reference/resample_chart.html
index db72e4e..58dbed4 100644
--- a/reference/resample_chart.html
+++ b/reference/resample_chart.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Plots the results of a resampling."><title>resample_chart class — resample_chart • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="resample_chart class — resample_chart"><meta property="og:description" content="Plots the results of a resampling."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
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+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
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       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>resample_chart class</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/resample_class.R" class="external-link"><code>R/resample_class.R</code></a></small>
+
+      <h1>resample_chart class</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/resample_class.R" class="external-link"><code>R/resample_class.R</code></a></small>
       <div class="d-none name"><code>resample_chart.Rd</code></div>
     </div>
 
@@ -59,23 +47,23 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>style</dt>
+
+
+<dl><dt id="arg-style">style<a class="anchor" aria-label="anchor" href="#arg-style"></a></dt>
 <dd><p>The plot style. One of 'boxplot', 'violin', 'histogram', 'density' or 'scatter'.</p></dd>
 
 
-<dt>binwidth</dt>
+<dt id="arg-binwidth">binwidth<a class="anchor" aria-label="anchor" href="#arg-binwidth"></a></dt>
 <dd><p>Binwidth for the "histogram" style. Ignored for all other styles.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>additional slots and values passed to struct_class</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>struct object</p>
+    <p>struct object</p>
     </div>
 
     <div class="section level2">
@@ -83,23 +71,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">C</span> <span class="op">=</span> <span class="fu">resample_chart</span><span class="op">(</span>style<span class="op">=</span><span class="st">'boxplot'</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
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+
 
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diff --git a/reference/rsd_filter.html b/reference/rsd_filter.html
index 8d77f40..75c750d 100644
--- a/reference/rsd_filter.html
+++ b/reference/rsd_filter.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="An RSD filter calculates the relative standard deviation (the ratio of the standard deviation to the mean) for all features. Any feature with an RSD greater than a predefined threshold is excluded."><title>RSD filter — rsd_filter • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="RSD filter — rsd_filter"><meta property="og:description" content="An RSD filter calculates the relative standard deviation (the ratio of the standard deviation to the mean) for all features. Any feature with an RSD greater than a predefined threshold is excluded."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-      </ul><form class="form-inline my-2 my-lg-0" role="search">
-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
-
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-  <a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="github">
-    <span class="fab fa fab fa-github fa-lg"></span>
-     
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-</li>
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>RSD filter</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/rsd_filter.R" class="external-link"><code>R/rsd_filter.R</code></a></small>
+
+      <h1>RSD filter</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/rsd_filter.R" class="external-link"><code>R/rsd_filter.R</code></a></small>
       <div class="d-none name"><code>rsd_filter.Rd</code></div>
     </div>
 
@@ -59,43 +47,42 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>rsd_threshold</dt>
+
+
+<dl><dt id="arg-rsd-threshold">rsd_threshold<a class="anchor" aria-label="anchor" href="#arg-rsd-threshold"></a></dt>
 <dd><p>(numeric) The RSD threshold above which features are removed. The default is <code>20</code>.<br></p></dd>
 
 
-<dt>qc_label</dt>
+<dt id="arg-qc-label">qc_label<a class="anchor" aria-label="anchor" href="#arg-qc-label"></a></dt>
 <dd><p>(character) The label used to identify QC samples. The default is <code>"QC"</code>.</p></dd>
 
 
-<dt>factor_name</dt>
+<dt id="arg-factor-name">factor_name<a class="anchor" aria-label="anchor" href="#arg-factor-name"></a></dt>
 <dd><p>(character) The name of a sample-meta column to use.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>rsd_filter</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>filtered</code></td><td>(DatasetExperiment) A DatasetExperiment object containing the filtered data.</td></tr><tr><td><code>flags</code></td><td>(data.frame) RSD and a flag indicating whether the feature was rejected by the filter or not.</td></tr><tr><td><code>rsd_qc</code></td><td>(data.frame) The calculated RSD of the QC class.</td></tr></table></div>
+    <p>A  <code>rsd_filter</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>filtered</code></td><td>(DatasetExperiment) A DatasetExperiment object containing the filtered data.</td></tr><tr><td><code>flags</code></td><td>(data.frame) RSD and a flag indicating whether the feature was rejected by the filter or not.</td></tr><tr><td><code>rsd_qc</code></td><td>(data.frame) The calculated RSD of the QC class.</td></tr></table></div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>pmp</code></p></li>
 </ul></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>rsd_filter</code> object inherits the following <code>struct</code> classes: <br><br><code>[rsd_filter]</code> &gt;&gt; <code>[model]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
     <div class="section level2">
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
-    <p>Jankevics A, Lloyd GR, Weber RJM (2023). <em>pmp: Peak Matrix Processing
-and signal batch correction for metabolomics datasets</em>.
-doi:10.18129/B9.bioc.pmp <a href="https://doi.org/10.18129/B9.bioc.pmp" class="external-link">https://doi.org/10.18129/B9.bioc.pmp</a>, R
-package version 1.14.0, <a href="https://bioconductor.org/packages/pmp" class="external-link">https://bioconductor.org/packages/pmp</a>.</p>
+    <p>Jankevics A, Lloyd GR, Weber RJM (????). <em>pmp: Peak Matrix Processing
+and signal batch correction for metabolomics datasets</em>. R package
+version 1.15.1.</p>
     </div>
 
     <div class="section level2">
@@ -109,23 +96,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
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+
 
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diff --git a/reference/rsd_filter_hist.html b/reference/rsd_filter_hist.html
index 47a8403..5a1a046 100644
--- a/reference/rsd_filter_hist.html
+++ b/reference/rsd_filter_hist.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>RSD histogram</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/rsd_filter.R" class="external-link"><code>R/rsd_filter.R</code></a></small>
+
+      <h1>RSD histogram</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/rsd_filter.R" class="external-link"><code>R/rsd_filter.R</code></a></small>
       <div class="d-none name"><code>rsd_filter_hist.Rd</code></div>
     </div>
 
@@ -59,22 +47,22 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>...</dt>
+
+
+<dl><dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>
+    <p>A  <code>
 rsd_filter_hist
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>rsd_filter_hist</code> object inherits the following <code>struct</code> classes: <br><br><code>[rsd_filter_hist]</code> &gt;&gt; <code>[chart]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -87,23 +75,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
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-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
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diff --git a/reference/run,forward_selection_by_rank,DatasetExperiment,metric-method.html b/reference/run,forward_selection_by_rank,DatasetExperiment,metric-method.html
new file mode 100644
index 0000000..3c841cc
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+++ b/reference/run,forward_selection_by_rank,DatasetExperiment,metric-method.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://computational-metabolomics.github.io/structToolbox/reference/run.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://computational-metabolomics.github.io/structToolbox/reference/run.html">
+  </head>
+</html>
+
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new file mode 100644
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+++ b/reference/run,grid_search_1d,DatasetExperiment,metric-method.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://computational-metabolomics.github.io/structToolbox/reference/run.html" />
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+
diff --git a/reference/run,kfold_xval,DatasetExperiment,metric-method.html b/reference/run,kfold_xval,DatasetExperiment,metric-method.html
new file mode 100644
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+++ b/reference/run,kfold_xval,DatasetExperiment,metric-method.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://computational-metabolomics.github.io/structToolbox/reference/run.html" />
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new file mode 100644
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+++ b/reference/run,permutation_test,DatasetExperiment,metric-method.html
@@ -0,0 +1,8 @@
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@@ -0,0 +1,8 @@
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diff --git a/reference/run,resample,DatasetExperiment,metric-method.html b/reference/run,resample,DatasetExperiment,metric-method.html
new file mode 100644
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+++ b/reference/run,resample,DatasetExperiment,metric-method.html
@@ -0,0 +1,8 @@
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+  </head>
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+
diff --git a/reference/run.html b/reference/run.html
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+++ b/reference/run.html
@@ -1,56 +1,44 @@
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Runs an iterator, applying the chosen model multiple times. — run,bootstrap,DatasetExperiment,metric-method • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Runs an iterator, applying the chosen model multiple times. — run,bootstrap,DatasetExperiment,metric-method"><meta name="description" content="Running an iterator will apply the iterator a number of times to a DatasetExperiment.
 For example, in cross-validation the same model is applied multiple times to
 the same data, splitting it into training and test sets. The input metric
-object can be calculated and collected for each iteration as an output."><title>Runs an iterator, applying the chosen model multiple times. — run,bootstrap,DatasetExperiment,metric-method • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Runs an iterator, applying the chosen model multiple times. — run,bootstrap,DatasetExperiment,metric-method"><meta property="og:description" content="Running an iterator will apply the iterator a number of times to a DatasetExperiment.
+object can be calculated and collected for each iteration as an output."><meta property="og:description" content="Running an iterator will apply the iterator a number of times to a DatasetExperiment.
 For example, in cross-validation the same model is applied multiple times to
 the same data, splitting it into training and test sets. The input metric
-object can be calculated and collected for each iteration as an output."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+object can be calculated and collected for each iteration as an output."></head><body>
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+
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.17.3</small>
+
 
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-      <img src="" class="logo" alt=""><h1>Runs an iterator, applying the chosen model multiple times.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/bootstrap_class.R" class="external-link"><code>R/bootstrap_class.R</code></a>, <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/forward_selection_by_rank_class.R" class="external-link"><code>R/forward_selection_by_rank_class.R</code></a>, <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/grid_search_1d_class.R" class="external-link"><code>R/grid_search_1d_class.R</code></a>, and 5 more</small>
+
+      <h1>Runs an iterator, applying the chosen model multiple times.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/bootstrap_class.R" class="external-link"><code>R/bootstrap_class.R</code></a>, <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/forward_selection_by_rank_class.R" class="external-link"><code>R/forward_selection_by_rank_class.R</code></a>, <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/grid_search_1d_class.R" class="external-link"><code>R/grid_search_1d_class.R</code></a>, and 5 more</small>
       <div class="d-none name"><code>run.Rd</code></div>
     </div>
 
@@ -63,47 +51,47 @@
 
     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S4 method for bootstrap,DatasetExperiment,metric</span></span>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S4 method for class 'bootstrap,DatasetExperiment,metric'</span></span>
 <span><span class="fu">run</span><span class="op">(</span><span class="va">I</span>, <span class="va">D</span>, MET <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for forward_selection_by_rank,DatasetExperiment,metric</span></span>
+<span><span class="co"># S4 method for class 'forward_selection_by_rank,DatasetExperiment,metric'</span></span>
 <span><span class="fu">run</span><span class="op">(</span><span class="va">I</span>, <span class="va">D</span>, <span class="va">MET</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for grid_search_1d,DatasetExperiment,metric</span></span>
+<span><span class="co"># S4 method for class 'grid_search_1d,DatasetExperiment,metric'</span></span>
 <span><span class="fu">run</span><span class="op">(</span><span class="va">I</span>, <span class="va">D</span>, <span class="va">MET</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for kfold_xval,DatasetExperiment,metric</span></span>
+<span><span class="co"># S4 method for class 'kfold_xval,DatasetExperiment,metric'</span></span>
 <span><span class="fu">run</span><span class="op">(</span><span class="va">I</span>, <span class="va">D</span>, MET <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for permutation_test,DatasetExperiment,metric</span></span>
+<span><span class="co"># S4 method for class 'permutation_test,DatasetExperiment,metric'</span></span>
 <span><span class="fu">run</span><span class="op">(</span><span class="va">I</span>, <span class="va">D</span>, MET <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for permute_sample_order,DatasetExperiment,metric</span></span>
+<span><span class="co"># S4 method for class 'permute_sample_order,DatasetExperiment,metric'</span></span>
 <span><span class="fu">run</span><span class="op">(</span><span class="va">I</span>, <span class="va">D</span>, <span class="va">MET</span><span class="op">)</span></span>
 <span></span>
-<span><span class="co"># S4 method for resample,DatasetExperiment,metric</span></span>
+<span><span class="co"># S4 method for class 'resample,DatasetExperiment,metric'</span></span>
 <span><span class="fu">run</span><span class="op">(</span><span class="va">I</span>, <span class="va">D</span>, <span class="va">MET</span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>I</dt>
+
+
+<dl><dt id="arg-i">I<a class="anchor" aria-label="anchor" href="#arg-i"></a></dt>
 <dd><p>an iterator object</p></dd>
 
 
-<dt>D</dt>
+<dt id="arg-d">D<a class="anchor" aria-label="anchor" href="#arg-d"></a></dt>
 <dd><p>a DatasetExperiment object</p></dd>
 
 
-<dt>MET</dt>
+<dt id="arg-met">MET<a class="anchor" aria-label="anchor" href="#arg-met"></a></dt>
 <dd><p>a metric object</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>Modified iterator object</p>
+    <p>Modified iterator object</p>
     </div>
 
     <div class="section level2">
@@ -116,23 +104,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>no calculation provided for this metric</span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
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+
 
   </body></html>
 
diff --git a/reference/sb_corr.html b/reference/sb_corr.html
index 1618a56..f36ef0b 100644
--- a/reference/sb_corr.html
+++ b/reference/sb_corr.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Applies Quality Control Robust Spline (QC-RSC) method to correct for signal drift and batch differences in mass spectrometry data."><title>Signal/batch correction for mass spectrometry data — sb_corr • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Signal/batch correction for mass spectrometry data — sb_corr"><meta property="og:description" content="Applies Quality Control Robust Spline (QC-RSC) method to correct for signal drift and batch differences in mass spectrometry data."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Signal/batch correction for mass spectrometry data — sb_corr • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Signal/batch correction for mass spectrometry data — sb_corr"><meta name="description" content="Applies Quality Control Robust Spline (QC-RSC) method to correct for signal drift and batch differences in mass spectrometry data."><meta property="og:description" content="Applies Quality Control Robust Spline (QC-RSC) method to correct for signal drift and batch differences in mass spectrometry data."></head><body>
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+
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.17.3</small>
+
 
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
-  <a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-articles">Articles</a>
-  <div class="dropdown-menu" aria-labelledby="dropdown-articles">
-    <a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a>
-  </div>
-</li>
-      </ul><form class="form-inline my-2 my-lg-0" role="search">
-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
-
-      <ul class="navbar-nav"><li class="nav-item">
-  <a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="github">
-    <span class="fab fa fab fa-github fa-lg"></span>
-     
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-</li>
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Signal/batch correction for mass spectrometry data</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/sb_corr.R" class="external-link"><code>R/sb_corr.R</code></a></small>
+
+      <h1>Signal/batch correction for mass spectrometry data</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/sb_corr.R" class="external-link"><code>R/sb_corr.R</code></a></small>
       <div class="d-none name"><code>sb_corr.Rd</code></div>
     </div>
 
@@ -69,50 +57,50 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>order_col</dt>
+
+
+<dl><dt id="arg-order-col">order_col<a class="anchor" aria-label="anchor" href="#arg-order-col"></a></dt>
 <dd><p>(character) The column name of sample_meta indicating the run order of the samples.</p></dd>
 
 
-<dt>batch_col</dt>
+<dt id="arg-batch-col">batch_col<a class="anchor" aria-label="anchor" href="#arg-batch-col"></a></dt>
 <dd><p>(character) The column name of sample_meta indicating the batch each sample was measured in.</p></dd>
 
 
-<dt>qc_col</dt>
+<dt id="arg-qc-col">qc_col<a class="anchor" aria-label="anchor" href="#arg-qc-col"></a></dt>
 <dd><p>(character) The column name of sample_meta indicating the group each sample is a member of.</p></dd>
 
 
-<dt>smooth</dt>
+<dt id="arg-smooth">smooth<a class="anchor" aria-label="anchor" href="#arg-smooth"></a></dt>
 <dd><p>(numeric) The amount of smoothing applied (0 to 1). If set to 0 the smoothing parameter will be estimated using
 leave-one-out cross-validation. The default is <code>0</code>.<br></p></dd>
 
 
-<dt>use_log</dt>
+<dt id="arg-use-log">use_log<a class="anchor" aria-label="anchor" href="#arg-use-log"></a></dt>
 <dd><p>(logical) Log tranformation. Allowed values are limited to the following:</p><ul><li><p><code>"TRUE"</code>: The data is log transformed prior to performing signal correction.</p></li>
 <li><p><code>"FALSE"</code>: Signal correction is applied to the input data.</p></li>
 </ul><p>The default is <code>TRUE</code>.<br></p></dd>
 
 
-<dt>min_qc</dt>
+<dt id="arg-min-qc">min_qc<a class="anchor" aria-label="anchor" href="#arg-min-qc"></a></dt>
 <dd><p>(numeric) The minimum number of QC samples required for signal correction. The default is <code>4</code>.<br></p></dd>
 
 
-<dt>qc_label</dt>
+<dt id="arg-qc-label">qc_label<a class="anchor" aria-label="anchor" href="#arg-qc-label"></a></dt>
 <dd><p>(character) The label used to identify QC samples. The default is <code>"QC"</code>.</p></dd>
 
 
-<dt>spar_lim</dt>
+<dt id="arg-spar-lim">spar_lim<a class="anchor" aria-label="anchor" href="#arg-spar-lim"></a></dt>
 <dd><p>(numeric) A two element vector specifying the upper and lower limits when <code>spar = 0</code>. Allows the value of <code>spar</code> to be constrained within these limits to prevent overfitting. The default is <code>c(-1.5, 1.5)</code>.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>sb_corr</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>corrected</code></td><td>(DatasetExperiment) The DatasetExperiment after signal/batch correction has been applied.</td></tr><tr><td><code>fitted</code></td><td>(data.frame) The fitted splines for each feature.</td></tr></table><p>struct object</p>
+    <p>A  <code>sb_corr</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>corrected</code></td><td>(DatasetExperiment) The DatasetExperiment after signal/batch correction has been applied.</td></tr><tr><td><code>fitted</code></td><td>(data.frame) The fitted splines for each feature.</td></tr></table><p>struct object</p>
     </div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
@@ -120,16 +108,15 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 </ul></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>sb_corr</code> object inherits the following <code>struct</code> classes: <br><br><code>[sb_corr]</code> &gt;&gt; <code>[model]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
     <div class="section level2">
     <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a></h2>
-    <p>Jankevics A, Lloyd GR, Weber RJM (2023). <em>pmp: Peak Matrix Processing
-and signal batch correction for metabolomics datasets</em>.
-doi:10.18129/B9.bioc.pmp <a href="https://doi.org/10.18129/B9.bioc.pmp" class="external-link">https://doi.org/10.18129/B9.bioc.pmp</a>, R
-package version 1.14.0, <a href="https://bioconductor.org/packages/pmp" class="external-link">https://bioconductor.org/packages/pmp</a>.</p>
+    <p>Jankevics A, Lloyd GR, Weber RJM (????). <em>pmp: Peak Matrix Processing
+and signal batch correction for metabolomics datasets</em>. R package
+version 1.15.1.</p>
 <p>Kirwan JA, Broadhurst DI, Davidson RL, Viant MR (2013). "Characterising
 and correcting batch variation in an automated direct infusion mass
 spectrometry (DIMS) metabolomics workflow." <em>Analytical and
@@ -151,23 +138,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">sb_corr</span><span class="op">(</span>order_col<span class="op">=</span><span class="st">'run_order'</span>,batch_col<span class="op">=</span><span class="st">'batch_no'</span>,qc_col<span class="op">=</span><span class="st">'class'</span><span class="op">)</span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/scatter_chart-1.png b/reference/scatter_chart-1.png
index da33b4f..18d9832 100644
Binary files a/reference/scatter_chart-1.png and b/reference/scatter_chart-1.png differ
diff --git a/reference/scatter_chart.html b/reference/scatter_chart.html
index 6fb6cab..ec113f8 100644
--- a/reference/scatter_chart.html
+++ b/reference/scatter_chart.html
@@ -1,50 +1,38 @@
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-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Group scatter chart — scatter_chart • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Group scatter chart — scatter_chart"><meta name="description" content="Plots a 2d scatter plot of the input data."><meta property="og:description" content="Plots a 2d scatter plot of the input data."></head><body>
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-    
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary"><div class="container">
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+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.17.3</small>
+
 
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
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+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
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+
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Group scatter chart</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/scatter_chart_class.R" class="external-link"><code>R/scatter_chart_class.R</code></a></small>
+
+      <h1>Group scatter chart</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/scatter_chart_class.R" class="external-link"><code>R/scatter_chart_class.R</code></a></small>
       <div class="d-none name"><code>scatter_chart.Rd</code></div>
     </div>
 
@@ -71,26 +59,28 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>xcol</dt>
+
+
+<dl><dt id="arg-xcol">xcol<a class="anchor" aria-label="anchor" href="#arg-xcol"></a></dt>
 <dd><p>(numeric, integer, character) The column name, or index, of data to plot on the x-axis. The default is <code>1</code>.<br></p></dd>
 
 
-<dt>ycol</dt>
+<dt id="arg-ycol">ycol<a class="anchor" aria-label="anchor" href="#arg-ycol"></a></dt>
 <dd><p>(numeric, integer, character) The column name, or index, of data to plot on the y-axis. The default is <code>2</code>.<br></p></dd>
 
 
-<dt>points_to_label</dt>
+<dt id="arg-points-to-label">points_to_label<a class="anchor" aria-label="anchor" href="#arg-points-to-label"></a></dt>
 <dd><p>(character) Points to label. Allowed values are limited to the following:</p><ul><li><p><code>"none"</code>: No samples labels are displayed.</p></li>
 <li><p><code>"all"</code>: The labels for all samples are displayed.</p></li>
 <li><p><code>"outliers"</code>: Labels for for potential outlier samples are displayed.</p></li>
 </ul><p>The default is <code>"none"</code>.</p></dd>
 
 
-<dt>factor_name</dt>
+<dt id="arg-factor-name">factor_name<a class="anchor" aria-label="anchor" href="#arg-factor-name"></a></dt>
 <dd><p>(character) The name of a sample-meta column to use. The default is <code>"none"</code>.</p></dd>
 
 
-<dt>ellipse</dt>
+<dt id="arg-ellipse">ellipse<a class="anchor" aria-label="anchor" href="#arg-ellipse"></a></dt>
 <dd><p>(character) Plot ellipses. Allowed values are limited to the following:</p><ul><li><p><code>"all"</code>: Ellipses are plotted for all groups and all samples.</p></li>
 <li><p><code>"group"</code>: Ellipses are plotted for all groups.</p></li>
 <li><p><code>"none"</code>: Ellipses are not included on the plot.</p></li>
@@ -98,44 +88,42 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 </ul><p>The default is <code>"all"</code>.</p></dd>
 
 
-<dt>ellipse_type</dt>
+<dt id="arg-ellipse-type">ellipse_type<a class="anchor" aria-label="anchor" href="#arg-ellipse-type"></a></dt>
 <dd><p>(character) Type of ellipse. Allowed values are limited to the following:</p><ul><li><p><code>"norm"</code>: Multivariate normal (p = 0.95).</p></li>
 <li><p><code>"t"</code>: Multivariate t (p = 0.95).</p></li>
 </ul><p>The default is <code>"norm"</code>.</p></dd>
 
 
-<dt>ellipse_confidence</dt>
+<dt id="arg-ellipse-confidence">ellipse_confidence<a class="anchor" aria-label="anchor" href="#arg-ellipse-confidence"></a></dt>
 <dd><p>(numeric) The confidence level for plotting ellipses. The default is <code>0.95</code>.<br></p></dd>
 
 
-<dt>label_filter</dt>
+<dt id="arg-label-filter">label_filter<a class="anchor" aria-label="anchor" href="#arg-label-filter"></a></dt>
 <dd><p>(character) Labels are only plotted for the named groups. If zero-length then all groups are included. The default is <code>character(0)</code>.</p></dd>
 
 
-<dt>label_factor</dt>
+<dt id="arg-label-factor">label_factor<a class="anchor" aria-label="anchor" href="#arg-label-factor"></a></dt>
 <dd><p>(character) The column name of sample_meta to use for labelling samples on the plot. "rownames" will use the row names from sample_meta. The default is <code>"rownames"</code>.</p></dd>
 
 
-<dt>label_size</dt>
+<dt id="arg-label-size">label_size<a class="anchor" aria-label="anchor" href="#arg-label-size"></a></dt>
 <dd><p>(numeric) The text size of labels. Note this is not in Font Units. The default is <code>3.88</code>.<br></p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>
+    <p>A  <code>
 scatter_chart
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>scatter_chart</code> object inherits the following <code>struct</code> classes: <br><br><code>[scatter_chart]</code> &gt;&gt; <code>[chart]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -165,23 +153,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
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diff --git a/reference/split_data.html b/reference/split_data.html
index 5a4a7d5..5a5f223 100644
--- a/reference/split_data.html
+++ b/reference/split_data.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="The data matrix is divided into two subsets.A predefined proportion of the samples are randomly selected for a training set, and the remaining samples are used for the test set."><title>Split data — split_data • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Split data — split_data"><meta property="og:description" content="The data matrix is divided into two subsets.A predefined proportion of the samples are randomly selected for a training set, and the remaining samples are used for the test set."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Split data — split_data • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Split data — split_data"><meta name="description" content="The data matrix is divided into two subsets.A predefined proportion of the samples are randomly selected for a training set, and the remaining samples are used for the test set."><meta property="og:description" content="The data matrix is divided into two subsets.A predefined proportion of the samples are randomly selected for a training set, and the remaining samples are used for the test set."></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-    
 
-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary"><div class="container">
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+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.17.3</small>
+
 
-    
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-  <a class="nav-link" href="../reference/index.html">Reference</a>
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
 <li class="nav-item dropdown">
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+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Split data</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/split_data_class.R" class="external-link"><code>R/split_data_class.R</code></a></small>
+
+      <h1>Split data</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/split_data_class.R" class="external-link"><code>R/split_data_class.R</code></a></small>
       <div class="d-none name"><code>split_data.Rd</code></div>
     </div>
 
@@ -59,22 +47,22 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>p_train</dt>
+
+
+<dl><dt id="arg-p-train">p_train<a class="anchor" aria-label="anchor" href="#arg-p-train"></a></dt>
 <dd><p>(numeric) The proportion of samples selected for the training set.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>split_data</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>training</code></td><td>(DatasetExperiment) A DatasetExperiment object containing samples selected for the training set.</td></tr><tr><td><code>testing</code></td><td>(DatasetExperiment) A DatasetExperiment object containing samples selected for the testing set.</td></tr></table></div>
+    <p>A  <code>split_data</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>training</code></td><td>(DatasetExperiment) A DatasetExperiment object containing samples selected for the training set.</td></tr><tr><td><code>testing</code></td><td>(DatasetExperiment) A DatasetExperiment object containing samples selected for the testing set.</td></tr></table></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>split_data</code> object inherits the following <code>struct</code> classes: <br><br><code>[split_data]</code> &gt;&gt; <code>[model]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -88,23 +76,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
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-  
 
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+
 
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diff --git a/reference/stratified_split.html b/reference/stratified_split.html
index 6f3c7fd..9d90a6c 100644
--- a/reference/stratified_split.html
+++ b/reference/stratified_split.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="The dataset is divided into two subsets. A predefined proportion of samples from each level of a factor is selected for the training set, and the remaining samples are used for the test set. The stratification by factor level means that the relative number of samples per level is approximately equal to the original dataset."><title>Stratified sampling — stratified_split • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Stratified sampling — stratified_split"><meta property="og:description" content="The dataset is divided into two subsets. A predefined proportion of samples from each level of a factor is selected for the training set, and the remaining samples are used for the test set. The stratification by factor level means that the relative number of samples per level is approximately equal to the original dataset."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Stratified sampling — stratified_split • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Stratified sampling — stratified_split"><meta name="description" content="The dataset is divided into two subsets. A predefined proportion of samples from each level of a factor is selected for the training set, and the remaining samples are used for the test set. The stratification by factor level means that the relative number of samples per level is approximately equal to the original dataset."><meta property="og:description" content="The dataset is divided into two subsets. A predefined proportion of samples from each level of a factor is selected for the training set, and the remaining samples are used for the test set. The stratification by factor level means that the relative number of samples per level is approximately equal to the original dataset."></head><body>
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+
 
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Stratified sampling</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/stratified_split_class.R" class="external-link"><code>R/stratified_split_class.R</code></a></small>
+
+      <h1>Stratified sampling</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/stratified_split_class.R" class="external-link"><code>R/stratified_split_class.R</code></a></small>
       <div class="d-none name"><code>stratified_split.Rd</code></div>
     </div>
 
@@ -59,26 +47,26 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>p_train</dt>
+
+
+<dl><dt id="arg-p-train">p_train<a class="anchor" aria-label="anchor" href="#arg-p-train"></a></dt>
 <dd><p>(numeric) The proportion of samples selected for the training set.</p></dd>
 
 
-<dt>factor_name</dt>
+<dt id="arg-factor-name">factor_name<a class="anchor" aria-label="anchor" href="#arg-factor-name"></a></dt>
 <dd><p>(character) The name of a sample-meta column to use.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>stratified_split</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>training</code></td><td>(DatasetExperiment) A DatasetExperiment object containing samples selected for the training set.</td></tr><tr><td><code>testing</code></td><td>(DatasetExperiment) A DatasetExperiment object containing samples selected for the testing set.</td></tr></table></div>
+    <p>A  <code>stratified_split</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>training</code></td><td>(DatasetExperiment) A DatasetExperiment object containing samples selected for the training set.</td></tr><tr><td><code>testing</code></td><td>(DatasetExperiment) A DatasetExperiment object containing samples selected for the testing set.</td></tr></table></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>stratified_split</code> object inherits the following <code>struct</code> classes: <br><br><code>[stratified_split]</code> &gt;&gt; <code>[split_data]</code> &gt;&gt; <code>[model]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -95,23 +83,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
+
+
 
   </body></html>
 
diff --git a/reference/structToolbox-package.html b/reference/structToolbox-package.html
new file mode 100644
index 0000000..5868e3b
--- /dev/null
+++ b/reference/structToolbox-package.html
@@ -0,0 +1,75 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>structToolbox: Examples of tools built using the Statistics in R Using Class Templates (struct) package — structToolbox-package • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="structToolbox: Examples of tools built using the Statistics in R Using Class Templates (struct) package — structToolbox-package"><meta name="description" content="This package extends the classes defined in the struct package"><meta property="og:description" content="This package extends the classes defined in the struct package"></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.17.3</small>
+
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+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>structToolbox: Examples of tools built using the Statistics in R Using Class Templates (struct) package</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/structToolbox.R" class="external-link"><code>R/structToolbox.R</code></a></small>
+      <div class="d-none name"><code>structToolbox-package.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>This package extends the classes defined in the struct package</p>
+    </div>
+
+
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index"><p>Useful links:</p><ul><li><p><a href="https://github.com/computational-metabolomics/structToolbox" class="external-link">https://github.com/computational-metabolomics/structToolbox</a></p></li>
+<li><p><a href="https://computational-metabolomics.github.io/structToolbox/">https://computational-metabolomics.github.io/structToolbox/</a></p></li>
+</ul></div>
+    </div>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
+    <p><strong>Maintainer</strong>: Gavin Rhys Lloyd <a href="mailto:g.r.lloyd@bham.ac.uk">g.r.lloyd@bham.ac.uk</a> (<a href="https://orcid.org/0000-0001-7989-6695" class="external-link">ORCID</a>)</p>
+<p>Authors:</p><ul><li><p>Ralf Johannes Maria Weber <a href="mailto:r.j.weber@bham.ac.uk">r.j.weber@bham.ac.uk</a></p></li>
+</ul></div>
+
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
+</div>
+
+    </footer></div>
+
+
+
+
+
+  </body></html>
+
diff --git a/reference/structToolbox.html b/reference/structToolbox.html
index 66fb5e9..6cfb9af 100644
--- a/reference/structToolbox.html
+++ b/reference/structToolbox.html
@@ -1,75 +1,8 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="This package extends the classes defined in the struct package"><title>structToolbox: Examples of tools built using the Statistics in R Using Class Templates (struct) package — structToolbox • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="structToolbox: Examples of tools built using the Statistics in R Using Class Templates (struct) package — structToolbox"><meta property="og:description" content="This package extends the classes defined in the struct package"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-  <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>structToolbox: Examples of tools built using the Statistics in R Using Class Templates (struct) package</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/structToolbox.R" class="external-link"><code>R/structToolbox.R</code></a></small>
-      <div class="d-none name"><code>structToolbox.Rd</code></div>
-    </div>
-
-    <div class="ref-description section level2">
-    <p>This package extends the classes defined in the struct package</p>
-    </div>
-
-
-
-  </main></div>
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-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
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diff --git a/reference/svm_plot_2d-1.png b/reference/svm_plot_2d-1.png
index f1de69a..01dcad8 100644
Binary files a/reference/svm_plot_2d-1.png and b/reference/svm_plot_2d-1.png differ
diff --git a/reference/svm_plot_2d.html b/reference/svm_plot_2d.html
index 20a4d95..d01829d 100644
--- a/reference/svm_plot_2d.html
+++ b/reference/svm_plot_2d.html
@@ -1,50 +1,38 @@
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   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>SVM scatter plot</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/svm_classifier_class.R" class="external-link"><code>R/svm_classifier_class.R</code></a></small>
+
+      <h1>SVM scatter plot</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/svm_classifier_class.R" class="external-link"><code>R/svm_classifier_class.R</code></a></small>
       <div class="d-none name"><code>svm_plot_2d.Rd</code></div>
     </div>
 
@@ -59,23 +47,23 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>factor_name</dt>
+
+
+<dl><dt id="arg-factor-name">factor_name<a class="anchor" aria-label="anchor" href="#arg-factor-name"></a></dt>
 <dd><p>(character) The name of a sample-meta column to use.</p></dd>
 
 
-<dt>npoints</dt>
+<dt id="arg-npoints">npoints<a class="anchor" aria-label="anchor" href="#arg-npoints"></a></dt>
 <dd><p>(numeric) The number of grid points used to plot the boundary. The default is <code>100</code>.<br></p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>
+    <p>A  <code>
 svm_plot_2d
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
@@ -86,7 +74,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 </ul></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>svm_plot_2d</code> object inherits the following <code>struct</code> classes: <br><br><code>[svm_plot_2d]</code> &gt;&gt; <code>[chart]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -116,23 +104,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
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-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
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diff --git a/reference/tSNE.html b/reference/tSNE.html
index 792bdcb..26f3bf1 100644
--- a/reference/tSNE.html
+++ b/reference/tSNE.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="t-Distributed Stochastic Neighbor Embedding."><title>tSNE — tSNE • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="tSNE — tSNE"><meta property="og:description" content="t-Distributed Stochastic Neighbor Embedding."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>tSNE — tSNE • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="tSNE — tSNE"><meta name="description" content="t-Distributed Stochastic Neighbor Embedding."><meta property="og:description" content="t-Distributed Stochastic Neighbor Embedding."></head><body>
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+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
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-      <img src="" class="logo" alt=""><h1>tSNE</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/tSNE_class.R" class="external-link"><code>R/tSNE_class.R</code></a></small>
+
+      <h1>tSNE</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/tSNE_class.R" class="external-link"><code>R/tSNE_class.R</code></a></small>
       <div class="d-none name"><code>tSNE.Rd</code></div>
     </div>
 
@@ -68,52 +56,52 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>dims</dt>
+
+
+<dl><dt id="arg-dims">dims<a class="anchor" aria-label="anchor" href="#arg-dims"></a></dt>
 <dd><p>(numeric) The number of tSNE dimensions computed. The default is <code>2</code>.<br></p></dd>
 
 
-<dt>perplexity</dt>
+<dt id="arg-perplexity">perplexity<a class="anchor" aria-label="anchor" href="#arg-perplexity"></a></dt>
 <dd><p>(numeric) Perplexity parameter. The default is <code>30</code>.<br></p></dd>
 
 
-<dt>max_iter</dt>
+<dt id="arg-max-iter">max_iter<a class="anchor" aria-label="anchor" href="#arg-max-iter"></a></dt>
 <dd><p>(numeric) The maximum number of tSNE iterations. The default is <code>100</code>.<br></p></dd>
 
 
-<dt>theta</dt>
+<dt id="arg-theta">theta<a class="anchor" aria-label="anchor" href="#arg-theta"></a></dt>
 <dd><p>(numeric) Speed/accuracy trade-off. A value of 0 gives an exact tSNE. The default is <code>0.5</code>.<br></p></dd>
 
 
-<dt>check_duplicates</dt>
+<dt id="arg-check-duplicates">check_duplicates<a class="anchor" aria-label="anchor" href="#arg-check-duplicates"></a></dt>
 <dd><p>(logical) Check for duplicates. Allowed values are limited to the following:</p><ul><li><p><code>"TRUE"</code>: Checks for the presence of exact duplicate samples.</p></li>
 <li><p><code>"FALSE"</code>: Does not check for exact duplicate samples.</p></li>
 </ul><p>The default is <code>FALSE</code>.<br></p></dd>
 
 
-<dt>init</dt>
+<dt id="arg-init">init<a class="anchor" aria-label="anchor" href="#arg-init"></a></dt>
 <dd><p>(NULL, data.frame, DatasetExperiment) A set of coordinates for initialising the tSNE algorithm. NULL uses random initialisation. The default is <code>NULL</code>.</p></dd>
 
 
-<dt>eta</dt>
+<dt id="arg-eta">eta<a class="anchor" aria-label="anchor" href="#arg-eta"></a></dt>
 <dd><p>(numeric) The learning rate parameter. The default is <code>200</code>.<br></p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>tSNE</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>Y</code></td><td>(DatasetExperiment)</td></tr></table></div>
+    <p>A  <code>tSNE</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>Y</code></td><td>(DatasetExperiment)</td></tr></table></div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
     <p>This object makes use of functionality from the following packages:</p><ul><li><p><code>Rtsne</code></p></li>
 </ul></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>tSNE</code> object inherits the following <code>struct</code> classes: <br><br><code>[tSNE]</code> &gt;&gt; <code>[model]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -143,23 +131,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
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diff --git a/reference/tSNE_scatter.html b/reference/tSNE_scatter.html
index fe8a3fc..0d840cf 100644
--- a/reference/tSNE_scatter.html
+++ b/reference/tSNE_scatter.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="plots the new representation of data after applying tSNE."><title>Feature boxplot — tSNE_scatter • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Feature boxplot — tSNE_scatter"><meta property="og:description" content="plots the new representation of data after applying tSNE."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Feature boxplot — tSNE_scatter • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Feature boxplot — tSNE_scatter"><meta name="description" content="plots the new representation of data after applying tSNE."><meta property="og:description" content="plots the new representation of data after applying tSNE."></head><body>
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     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
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 <li class="nav-item dropdown">
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+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Feature boxplot</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/tSNE_class.R" class="external-link"><code>R/tSNE_class.R</code></a></small>
+
+      <h1>Feature boxplot</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/tSNE_class.R" class="external-link"><code>R/tSNE_class.R</code></a></small>
       <div class="d-none name"><code>tSNE_scatter.Rd</code></div>
     </div>
 
@@ -59,19 +47,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>factor_name</dt>
+
+
+<dl><dt id="arg-factor-name">factor_name<a class="anchor" aria-label="anchor" href="#arg-factor-name"></a></dt>
 <dd><p>(character) The name of a sample-meta column to use.</p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>
+    <p>A  <code>
 tSNE_scatter
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
@@ -82,7 +70,7 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 </ul></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>tSNE_scatter</code> object inherits the following <code>struct</code> classes: <br><br><code>[tSNE_scatter]</code> &gt;&gt; <code>[chart]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -106,23 +94,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
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+
 
   </body></html>
 
diff --git a/reference/tic_chart-1.png b/reference/tic_chart-1.png
index f045475..833a0b4 100644
Binary files a/reference/tic_chart-1.png and b/reference/tic_chart-1.png differ
diff --git a/reference/tic_chart.html b/reference/tic_chart.html
index 9dfa570..32f6c67 100644
--- a/reference/tic_chart.html
+++ b/reference/tic_chart.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="A scatter plot of Total Ion Count (sum of each sample) versus run order."><title>Total Ion Count chart. — tic_chart • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Total Ion Count chart. — tic_chart"><meta property="og:description" content="A scatter plot of Total Ion Count (sum of each sample) versus run order."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Total Ion Count chart. — tic_chart • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Total Ion Count chart. — tic_chart"><meta name="description" content="A scatter plot of Total Ion Count (sum of each sample) versus run order."><meta property="og:description" content="A scatter plot of Total Ion Count (sum of each sample) versus run order."></head><body>
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     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
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 <li class="nav-item dropdown">
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-    <a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a>
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-</li>
-      </ul><form class="form-inline my-2 my-lg-0" role="search">
-        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
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-    <span class="fab fa fab fa-github fa-lg"></span>
-     
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_analysis_omics_using_the_structtoolbox.html">Data analysis of metabolomics and other omics datasets using the structToolbox</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul></div>
 
-    
+
   </div>
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Total Ion Count chart.</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/tic_chart_class.R" class="external-link"><code>R/tic_chart_class.R</code></a></small>
+
+      <h1>Total Ion Count chart.</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/tic_chart_class.R" class="external-link"><code>R/tic_chart_class.R</code></a></small>
       <div class="d-none name"><code>tic_chart.Rd</code></div>
     </div>
 
@@ -59,34 +47,34 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>run_order</dt>
+
+
+<dl><dt id="arg-run-order">run_order<a class="anchor" aria-label="anchor" href="#arg-run-order"></a></dt>
 <dd><p>(character) The column name of sample_meta indicating the run order of the samples.</p></dd>
 
 
-<dt>factor_name</dt>
+<dt id="arg-factor-name">factor_name<a class="anchor" aria-label="anchor" href="#arg-factor-name"></a></dt>
 <dd><p>(character) The name of a sample-meta column to use.</p></dd>
 
 
-<dt>connected</dt>
+<dt id="arg-connected">connected<a class="anchor" aria-label="anchor" href="#arg-connected"></a></dt>
 <dd><p>(logical) Plot samples connected by a grey line. The default is <code>FALSE</code>.<br></p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>
+    <p>A  <code>
 tic_chart
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>tic_chart</code> object inherits the following <code>struct</code> classes: <br><br><code>[tic_chart]</code> &gt;&gt; <code>[chart]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -106,23 +94,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
     </footer></div>
 
-  
 
-  
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   </body></html>
 
diff --git a/reference/ttest.html b/reference/ttest.html
index 938db21..b022855 100644
--- a/reference/ttest.html
+++ b/reference/ttest.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="A t-test compares the means of two factor levels. Multiple-test corrected p-values are used to indicate the significance of the computed difference for all features."><title>t-test — ttest • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="t-test — ttest"><meta property="og:description" content="A t-test compares the means of two factor levels. Multiple-test corrected p-values are used to indicate the significance of the computed difference for all features."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>t-test — ttest • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="t-test — ttest"><meta name="description" content="A t-test compares the means of two factor levels. Multiple-test corrected p-values are used to indicate the significance of the computed difference for all features."><meta property="og:description" content="A t-test compares the means of two factor levels. Multiple-test corrected p-values are used to indicate the significance of the computed difference for all features."></head><body>
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-    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary"><div class="container">
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     <a class="navbar-brand me-2" href="../index.html">structToolbox</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.15.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.17.3</small>
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+      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
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+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>t-test</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/ttest_class.R" class="external-link"><code>R/ttest_class.R</code></a></small>
+
+      <h1>t-test</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/ttest_class.R" class="external-link"><code>R/ttest_class.R</code></a></small>
       <div class="d-none name"><code>ttest.Rd</code></div>
     </div>
 
@@ -62,57 +50,62 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 <span>  paired_factor <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/character.html" class="external-link">character</a></span><span class="op">(</span><span class="fl">0</span><span class="op">)</span>,</span>
 <span>  equal_variance <span class="op">=</span> <span class="cn">FALSE</span>,</span>
 <span>  conf_level <span class="op">=</span> <span class="fl">0.95</span>,</span>
+<span>  control_group <span class="op">=</span> <span class="cn">NULL</span>,</span>
 <span>  <span class="va">...</span></span>
 <span><span class="op">)</span></span></code></pre></div>
     </div>
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>alpha</dt>
+
+
+<dl><dt id="arg-alpha">alpha<a class="anchor" aria-label="anchor" href="#arg-alpha"></a></dt>
 <dd><p>(numeric) The p-value cutoff for determining significance. The default is <code>0.05</code>.<br></p></dd>
 
 
-<dt>mtc</dt>
+<dt id="arg-mtc">mtc<a class="anchor" aria-label="anchor" href="#arg-mtc"></a></dt>
 <dd><p>(character) Multiple test correction method. Allowed values are limited to the following:</p><ul><li><p><code>"bonferroni"</code>: Bonferroni correction in which the p-values are multiplied by the number of comparisons.</p></li>
 <li><p><code>"fdr"</code>: Benjamini and Hochberg False Discovery Rate correction.</p></li>
 <li><p><code>"none"</code>: No correction.</p></li>
 </ul><p>The default is <code>"fdr"</code>.</p></dd>
 
 
-<dt>factor_names</dt>
+<dt id="arg-factor-names">factor_names<a class="anchor" aria-label="anchor" href="#arg-factor-names"></a></dt>
 <dd><p>(character) The name of sample meta column(s) to use.</p></dd>
 
 
-<dt>paired</dt>
+<dt id="arg-paired">paired<a class="anchor" aria-label="anchor" href="#arg-paired"></a></dt>
 <dd><p>(logical) Apply a paired t-test. The default is <code>FALSE</code>.<br></p></dd>
 
 
-<dt>paired_factor</dt>
+<dt id="arg-paired-factor">paired_factor<a class="anchor" aria-label="anchor" href="#arg-paired-factor"></a></dt>
 <dd><p>(character) The factor name that encodes the sample id for pairing. The default is <code>character(0)</code>.</p></dd>
 
 
-<dt>equal_variance</dt>
+<dt id="arg-equal-variance">equal_variance<a class="anchor" aria-label="anchor" href="#arg-equal-variance"></a></dt>
 <dd><p>(logical) Equal variance. Allowed values are limited to the following:</p><ul><li><p><code>"TRUE"</code>: The variance of each group is treated as being equal using the pooled variance to estimate the variance.</p></li>
 <li><p><code>"FALSE"</code>: The variance of each group is not assumed to be equal and the Welch (or Satterthwaite) approximation is used.</p></li>
 </ul><p>The default is <code>FALSE</code>.<br></p></dd>
 
 
-<dt>conf_level</dt>
+<dt id="arg-conf-level">conf_level<a class="anchor" aria-label="anchor" href="#arg-conf-level"></a></dt>
 <dd><p>(numeric) The confidence level of the interval. The default is <code>0.95</code>.<br></p></dd>
 
 
-<dt>...</dt>
+<dt id="arg-control-group">control_group<a class="anchor" aria-label="anchor" href="#arg-control-group"></a></dt>
+<dd><p>(character, NULL) The level name of the group used as the second group (where possible) when computing t-statistics. This ensures a positive t-statistic corresponds to an increase when compared to the control group. The default is <code>NULL</code>.</p></dd>
+
+
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>ttest</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>t_statistic</code></td><td>(data.frame) The value of the calculate statistics which is converted to a p-value when compared to a t-distribution.</td></tr><tr><td><code>p_value</code></td><td>(data.frame) The probability of observing the calculated t-statistic.</td></tr><tr><td><code>dof</code></td><td>(numeric) The number of degrees of freedom used to calculate the test statistic.</td></tr><tr><td><code>significant</code></td><td>(data.frame) TRUE if the calculated p-value is less than the supplied threhold (alpha).</td></tr><tr><td><code>conf_int</code></td><td>(data.frame) Confidence interval for t statistic.</td></tr><tr><td><code>estimates</code></td><td>(data.frame) The group means estimated when computing the t-statistic.</td></tr></table></div>
+    <p>A  <code>ttest</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>t_statistic</code></td><td>(data.frame) The value of the calculate statistics which is converted to a p-value when compared to a t-distribution.</td></tr><tr><td><code>p_value</code></td><td>(data.frame) The probability of observing the calculated t-statistic.</td></tr><tr><td><code>dof</code></td><td>(numeric) The number of degrees of freedom used to calculate the test statistic.</td></tr><tr><td><code>significant</code></td><td>(data.frame) TRUE if the calculated p-value is less than the supplied threhold (alpha).</td></tr><tr><td><code>conf_int</code></td><td>(data.frame) Confidence interval for t statistic.</td></tr><tr><td><code>estimates</code></td><td>(data.frame) The group means estimated when computing the t-statistic.</td></tr></table></div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>ttest</code> object inherits the following <code>struct</code> classes: <br><br><code>[ttest]</code> &gt;&gt; <code>[model]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -126,29 +119,30 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span>      paired <span class="op">=</span> <span class="cn">FALSE</span>,</span></span>
 <span class="r-in"><span>      paired_factor <span class="op">=</span> <span class="st">"NA"</span>,</span></span>
 <span class="r-in"><span>      equal_variance <span class="op">=</span> <span class="cn">FALSE</span>,</span></span>
-<span class="r-in"><span>      conf_level <span class="op">=</span> <span class="fl">0.95</span><span class="op">)</span></span></span>
+<span class="r-in"><span>      conf_level <span class="op">=</span> <span class="fl">0.95</span>,</span></span>
+<span class="r-in"><span>      control_group <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="va">M</span> <span class="op">=</span> <span class="fu">ttest</span><span class="op">(</span>factor_name<span class="op">=</span><span class="st">'Class'</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
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diff --git a/reference/vec_norm.html b/reference/vec_norm.html
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--- a/reference/vec_norm.html
+++ b/reference/vec_norm.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="The samples in the data matrix are normalised to account for differences in concentration by scaling each sample such that the sum of squares is equal to 1."><title>Vector normalisation — vec_norm • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Vector normalisation — vec_norm"><meta property="og:description" content="The samples in the data matrix are normalised to account for differences in concentration by scaling each sample such that the sum of squares is equal to 1."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-      <img src="" class="logo" alt=""><h1>Vector normalisation</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/vec_norm_class.R" class="external-link"><code>R/vec_norm_class.R</code></a></small>
+
+      <h1>Vector normalisation</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/vec_norm_class.R" class="external-link"><code>R/vec_norm_class.R</code></a></small>
       <div class="d-none name"><code>vec_norm.Rd</code></div>
     </div>
 
@@ -59,19 +47,19 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>...</dt>
+
+
+<dl><dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>vec_norm</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>normalised</code></td><td>(DatasetExperiment) A DatasetExperiment object containing the normalised data.</td></tr><tr><td><code>coeff</code></td><td>(data.frame) The normalisation coefficients calculated by PQN.</td></tr></table><p>struct object</p>
+    <p>A  <code>vec_norm</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>normalised</code></td><td>(DatasetExperiment) A DatasetExperiment object containing the normalised data.</td></tr><tr><td><code>coeff</code></td><td>(data.frame) The normalisation coefficients calculated by PQN.</td></tr></table><p>struct object</p>
     </div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>vec_norm</code> object inherits the following <code>struct</code> classes: <br><br><code>[vec_norm]</code> &gt;&gt; <code>[model]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -84,23 +72,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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diff --git a/reference/wilcox_p_hist.html b/reference/wilcox_p_hist.html
index f91cfa1..ac8dbad 100644
--- a/reference/wilcox_p_hist.html
+++ b/reference/wilcox_p_hist.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="A histogram of p values for the wilcoxon signed rank test"><title>Histogram of p values — wilcox_p_hist • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Histogram of p values — wilcox_p_hist"><meta property="og:description" content="A histogram of p values for the wilcoxon signed rank test"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
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-<![endif]--></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Histogram of p values — wilcox_p_hist • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Histogram of p values — wilcox_p_hist"><meta name="description" content="A histogram of p values for the wilcoxon signed rank test"><meta property="og:description" content="A histogram of p values for the wilcoxon signed rank test"></head><body>
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+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/computational-metabolomics/structToolbox/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
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-      <img src="" class="logo" alt=""><h1>Histogram of p values</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/wilcox_test_class.R" class="external-link"><code>R/wilcox_test_class.R</code></a></small>
+
+      <h1>Histogram of p values</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/wilcox_test_class.R" class="external-link"><code>R/wilcox_test_class.R</code></a></small>
       <div class="d-none name"><code>wilcox_p_hist.Rd</code></div>
     </div>
 
@@ -59,22 +47,22 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>...</dt>
+
+
+<dl><dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>
+    <p>A  <code>
 wilcox_p_hist
 </code> object. This object has no <code>output</code> slots.
 See <code><a href="https://rdrr.io/pkg/struct/man/chart_plot.html" class="external-link">chart_plot</a></code> in the <code>struct</code> package to plot this chart object.</p>
     </div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>wilcox_p_hist</code> object inherits the following <code>struct</code> classes: <br><br><code>[wilcox_p_hist]</code> &gt;&gt; <code>[chart]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -87,23 +75,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
 </div>
 
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diff --git a/reference/wilcox_test.html b/reference/wilcox_test.html
index c6c5560..9fabc47 100644
--- a/reference/wilcox_test.html
+++ b/reference/wilcox_test.html
@@ -1,50 +1,38 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="A Mann-Whitney-Wilcoxon signed rank test compares ,the ranks of values in two groups. It is the non-parametric equivalent of a t-test. Multiple test corrected p-values are computed as indicators of significance for each variable/feature."><title>wilcoxon signed rank test — wilcox_test • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="wilcoxon signed rank test — wilcox_test"><meta property="og:description" content="A Mann-Whitney-Wilcoxon signed rank test compares ,the ranks of values in two groups. It is the non-parametric equivalent of a t-test. Multiple test corrected p-values are computed as indicators of significance for each variable/feature."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>wilcoxon signed rank test — wilcox_test • structToolbox</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="wilcoxon signed rank test — wilcox_test"><meta name="description" content="A Mann-Whitney-Wilcoxon signed rank test compares ,the ranks of values in two groups. It is the non-parametric equivalent of a t-test. Multiple test corrected p-values are computed as indicators of significance for each variable/feature."><meta property="og:description" content="A Mann-Whitney-Wilcoxon signed rank test compares ,the ranks of values in two groups. It is the non-parametric equivalent of a t-test. Multiple test corrected p-values are computed as indicators of significance for each variable/feature."></head><body>
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-      <img src="" class="logo" alt=""><h1>wilcoxon signed rank test</h1>
-      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/HEAD/R/wilcox_test_class.R" class="external-link"><code>R/wilcox_test_class.R</code></a></small>
+
+      <h1>wilcoxon signed rank test</h1>
+      <small class="dont-index">Source: <a href="https://github.com/computational-metabolomics/structToolbox/blob/devel/R/wilcox_test_class.R" class="external-link"><code>R/wilcox_test_class.R</code></a></small>
       <div class="d-none name"><code>wilcox_test.Rd</code></div>
     </div>
 
@@ -67,46 +55,46 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-    <dl><dt>alpha</dt>
+
+
+<dl><dt id="arg-alpha">alpha<a class="anchor" aria-label="anchor" href="#arg-alpha"></a></dt>
 <dd><p>(numeric) The p-value cutoff for determining significance. The default is <code>0.05</code>.<br></p></dd>
 
 
-<dt>mtc</dt>
+<dt id="arg-mtc">mtc<a class="anchor" aria-label="anchor" href="#arg-mtc"></a></dt>
 <dd><p>(character) Multiple test correction method. Allowed values are limited to the following:</p><ul><li><p><code>"bonferroni"</code>: Bonferroni correction in which the p-values are multiplied by the number of comparisons.</p></li>
 <li><p><code>"fdr"</code>: Benjamini and Hochberg False Discovery Rate correction.</p></li>
 <li><p><code>"none"</code>: No correction.</p></li>
 </ul><p>The default is <code>"fdr"</code>.</p></dd>
 
 
-<dt>factor_names</dt>
+<dt id="arg-factor-names">factor_names<a class="anchor" aria-label="anchor" href="#arg-factor-names"></a></dt>
 <dd><p>(character) The name of a sample-meta column to use.</p></dd>
 
 
-<dt>paired</dt>
+<dt id="arg-paired">paired<a class="anchor" aria-label="anchor" href="#arg-paired"></a></dt>
 <dd><p>(logical) Apply a paired test. The default is <code>FALSE</code>.<br></p></dd>
 
 
-<dt>paired_factor</dt>
+<dt id="arg-paired-factor">paired_factor<a class="anchor" aria-label="anchor" href="#arg-paired-factor"></a></dt>
 <dd><p>(character) The factor name containing sample ids for paired data. The default is <code>character(0)</code>.</p></dd>
 
 
-<dt>conf_level</dt>
+<dt id="arg-conf-level">conf_level<a class="anchor" aria-label="anchor" href="#arg-conf-level"></a></dt>
 <dd><p>(numeric) The confidence level of the interval. The default is <code>0.95</code>.<br></p></dd>
 
 
-<dt>...</dt>
+<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
 <dd><p>Additional slots and values passed to <code>struct_class</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    
-
-<p>A  <code>wilcox_test</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>statistic</code></td><td>(data.frame) The value of the calculated statistic which is converted to a p-value.</td></tr><tr><td><code>p_value</code></td><td>(data.frame) The probability of observing the calculated t-statistic.</td></tr><tr><td><code>dof</code></td><td>(numeric) The number of degrees of freedom used to calculate the test statistic.</td></tr><tr><td><code>significant</code></td><td>(data.frame) TRUE if the calculated p-value is less than the supplied threhold (alpha).</td></tr><tr><td><code>conf_int</code></td><td>(data.frame) Confidence interval for t statistic.</td></tr><tr><td><code>estimates</code></td><td>(data.frame) The group estimates used when computing the statistic.</td></tr></table><p>struct object</p>
+    <p>A  <code>wilcox_test</code> object with the following <code>output</code> slots:</p><table class="table table"><tr><td><code>statistic</code></td><td>(data.frame) The value of the calculated statistic which is converted to a p-value.</td></tr><tr><td><code>p_value</code></td><td>(data.frame) The probability of observing the calculated t-statistic.</td></tr><tr><td><code>dof</code></td><td>(numeric) The number of degrees of freedom used to calculate the test statistic.</td></tr><tr><td><code>significant</code></td><td>(data.frame) TRUE if the calculated p-value is less than the supplied threhold (alpha).</td></tr><tr><td><code>conf_int</code></td><td>(data.frame) Confidence interval for t statistic.</td></tr><tr><td><code>estimates</code></td><td>(data.frame) The group estimates used when computing the statistic.</td></tr></table><p>struct object</p>
     </div>
     <div class="section level2">
     <h2 id="inheritance">Inheritance<a class="anchor" aria-label="anchor" href="#inheritance"></a></h2>
-    
+
 
 <p>A <code>wilcox_test</code> object inherits the following <code>struct</code> classes: <br><br><code>[wilcox_test]</code> &gt;&gt; <code>[model]</code> &gt;&gt; <code>[struct_class]</code></p>
     </div>
@@ -125,23 +113,23 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
-  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
+  <p>Developed by Gavin Rhys Lloyd, Ralf Johannes Maria Weber.</p>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.9000.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.9000.</p>
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   </body></html>
 
diff --git a/search.json b/search.json
index 898ecd5..ac88c28 100644
--- a/search.json
+++ b/search.json
@@ -1 +1 @@
-[{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"‘structToolbox’ includes extensive set data (pre-)processing analysis tools metabolomics omics, strong emphasis statistics machine learning. methods tools implemented using class-based templates available via struct (Statistics R Using Class-based Templates) package. aim vignette introduce reader basic advanced structToolbox-based operations implementations, use struct objects, getting/setting methods/parameters, building workflows analysis mass spectrometry (MS) nuclear magnetic resonance (NMR)-based Metabolomics proteomics datasets. workflows demonstrated include wide range methods tools including pre-processing filtering, normalisation scaling, followed univariate /multivariate statistics, machine learning approaches.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"getting-started","dir":"Articles","previous_headings":"","what":"Getting started","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"latest version structToolbox compatible current R version can installed using BiocManager. number additional packages needed vignette.","code":"# install BiocManager if not present if (!requireNamespace(\"BiocManager\", quietly = TRUE))     install.packages(\"BiocManager\")  # install structToolbox and dependencies BiocManager::install(\"structToolbox\") ## install additional bioc packages for vignette if needed #BiocManager::install(c('pmp', 'ropls', 'BiocFileCache'))  ## install additional CRAN packages if needed #install.packages(c('cowplot', 'openxlsx'))  suppressPackageStartupMessages({     # Bioconductor packages     library(structToolbox)     library(pmp)     library(ropls)     library(BiocFileCache)        # CRAN libraries     library(ggplot2)     library(gridExtra)     library(cowplot)     library(openxlsx) })   # use the BiocFileCache bfc <- BiocFileCache(ask = FALSE)"},{"path":[]},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"introduction-1","dir":"Articles","previous_headings":"Introduction to struct objects, including models, model sequences, model charts and ontology.","what":"Introduction","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"PCA (Principal Component Analysis) PLS (Partial Least Squares) commonly applied methods exploring analysing multivariate datasets. use two statistical methods demonstrate different types struct (STatistics R Using Class Templates) objects available part structToolbox objects (.e. class templates) can used conduct unsupervised supervised multivariate statistical analysis.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"dataset","dir":"Articles","previous_headings":"Introduction to struct objects, including models, model sequences, model charts and ontology.","what":"Dataset","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"demonstration purposes use “Iris” dataset. famous (Fisher’s Anderson’s) dataset contains measurements sepal length width petal length width, centimeters, 50 flowers 3 class Iris. class Iris setosa, versicolor, virginica. See (https://stat.ethz.ch/R-manual/R-devel/library/datasets/html/iris.html) information. Note: vignette also compatible Direct infusion mass spectrometry metabolomics “benchmark” dataset described Kirwan et al., Sci Data 1, 140012 (2014) (https://doi.org/10.1038/sdata.2014.12). datasets available part structToolbox package already prepared DatasetExperiment object.","code":"## Iris dataset (comment if using MTBLS79 benchmark data) D = iris_DatasetExperiment() D$sample_meta$class = D$sample_meta$Species  ## MTBLS (comment if using Iris data) # D = MTBLS79_DatasetExperiment(filtered=TRUE) # M = pqn_norm(qc_label='QC',factor_name='sample_type') +  #   knn_impute(neighbours=5) + #   glog_transform(qc_label='QC',factor_name='sample_type') + #   filter_smeta(mode='exclude',levels='QC',factor_name='sample_type') # M = model_apply(M,D) # D = predicted(M)  # show info D ## A \"DatasetExperiment\" object ## ---------------------------- ## name:          Fisher's Iris dataset ## description:   This famous (Fisher's or Anderson's) iris data set gives the measurements in centimeters of ##                  the variables sepal length and width and petal length and width, ##                  respectively, for 50 flowers from each of 3 species of iris. The species are ##                  Iris setosa, versicolor, and virginica. ## data:          150 rows x 4 columns ## sample_meta:   150 rows x 2 columns ## variable_meta: 4 rows x 1 columns"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"datasetexperiment-objects","dir":"Articles","previous_headings":"Introduction to struct objects, including models, model sequences, model charts and ontology. > Dataset","what":"DatasetExperiment objects","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"DatasetExperiment object extension SummarizedExperiment class used Bioconductor community. contains three main parts: data data frame containing measured data sample. sample_meta data frame additional information related samples e.g. group labels. variable_meta data frame additional information related variables (features) e.g. annotations Like struct objects also contains name description fields (called “slots” R language). key difference DatasetExperiment SummarizedExperiment objects data transposed. .e. DatasetExperiment objects samples rows features columns, opposite true SummarizedExperiment objects. slots accessible using dollar notation.","code":"# show some data head(D$data[,1:4]) ##   Sepal.Length Sepal.Width Petal.Length Petal.Width ## 1          5.1         3.5          1.4         0.2 ## 2          4.9         3.0          1.4         0.2 ## 3          4.7         3.2          1.3         0.2 ## 4          4.6         3.1          1.5         0.2 ## 5          5.0         3.6          1.4         0.2 ## 6          5.4         3.9          1.7         0.4"},{"path":[]},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"statistical-models","dir":"Articles","previous_headings":"Introduction to struct objects, including models, model sequences, model charts and ontology. > Using struct model objects","what":"Statistical models","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"can apply e.g. PCA first need create PCA object. object contains inputs, outputs methods needed apply PCA. can set parameters number components PCA model created, can also use dollar notation change/view later. inputs model can listed using param_ids(object): summary object can printed console:","code":"P = PCA(number_components=15) P$number_components=5 P$number_components ## [1] 5 param_ids(P) ## [1] \"number_components\" P ## A \"PCA\" object ## -------------- ## name:          Principal Component Analysis (PCA) ## description:   PCA is a multivariate data reduction technique. It summarises the data in a smaller number of ##                  Principal Components that maximise variance. ## input params:  number_components  ## outputs:       scores, loadings, eigenvalues, ssx, correlation, that  ## predicted:     that ## seq_in:        data"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"model-sequences","dir":"Articles","previous_headings":"Introduction to struct objects, including models, model sequences, model charts and ontology. > Using struct model objects","what":"Model sequences","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"Unless good reason , usually sensible mean centre columns data PCA. Using STRUCT framework can create model sequence mean centre apply PCA mean centred data. structToolbox mean centring PCA model objects, joining using “+” creates model_sequence object. model_sequence outputs first object (mean centring) automatically passed inputs second object (PCA), allows chain together modelling steps order build workflow. objects model_sequence can accessed indexing, can combine dollar notation. example, PCA object second object sequence can access number components follows:","code":"M = mean_centre() + PCA(number_components = 4) M[2]$number_components ## [1] 4"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"trainingtesting-models","dir":"Articles","previous_headings":"Introduction to struct objects, including models, model sequences, model charts and ontology. > Using struct model objects","what":"Training/testing models","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"Model model_sequence objects need trained using data form DatasetExperiment object. example, PCA model sequence created (M) can trained using iris DatasetExperiment object (‘D’). model sequence now mean centred original data calculated PCA scores loadings. Model objects can used generate predictions test datasets. PCA model sequence involves mean centring test data using mean training data, projecting centred test data onto PCA model using loadings. outputs stored model sequence can accessed using dollar notation. example just use training data (sometimes called autoprediction), PCA allows us explore training data detail. Sometimes models don’t make use training/test approach e.g. univariate statsitics, filtering etc. models model_apply method can used instead. models provide training/test methods, model_apply applies autoprediction default .e. short-cut applying model_train model_predict data. available outputs object can listed accessed like input params, using dollar notation:","code":"M = model_train(M,D) M = model_predict(M,D) M = model_apply(M,D) output_ids(M[2]) ## [1] \"scores\"      \"loadings\"    \"eigenvalues\" \"ssx\"         \"correlation\" ## [6] \"that\" M[2]$scores ## A \"DatasetExperiment\" object ## ---------------------------- ## name:           ## description:    ## data:          150 rows x 4 columns ## sample_meta:   150 rows x 2 columns ## variable_meta: 4 rows x 1 columns"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"model-charts","dir":"Articles","previous_headings":"Introduction to struct objects, including models, model sequences, model charts and ontology. > Using struct model objects","what":"Model charts","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"struct framework includes chart objects. Charts associated model object can listed. Like model objects, chart objects need created can used. plot PCA scores plot mean centred PCA model.  Note indexing PCA model required pca_scores_plot object requires PCA object input, model_sequence. make changes input parameters chart, chart_plot must called see effects.  chart_plot method returns ggplot object can easily combine plots using gridExtra cowplot packages example.","code":"chart_names(M[2]) ## [1] \"pca_biplot\"           \"pca_correlation_plot\" \"pca_dstat_plot\"       ## [4] \"pca_loadings_plot\"    \"pca_scores_plot\"      \"pca_scree_plot\" C = pca_scores_plot(factor_name='class') # colour by class chart_plot(C,M[2]) # add petal width to meta data of pca scores M[2]$scores$sample_meta$example=D$data[,1] # update plot C$factor_name='example' chart_plot(C,M[2]) # scores plot C1 = pca_scores_plot(factor_name='class') # colour by class g1 = chart_plot(C1,M[2])  # scree plot C2 = pca_scree_plot() g2 = chart_plot(C2,M[2])  # arange in grid grid.arrange(grobs=list(g1,g2),nrow=1)"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"ontology","dir":"Articles","previous_headings":"Introduction to struct objects, including models, model sequences, model charts and ontology. > Using struct model objects","what":"Ontology","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"Within struct framework (structToolbox) ontology slot provided allow standardardised definitions objects inputs outputs using Ontology Lookup Service (OLS). example, STATO general purpose STATistics Ontology (http://stato-ontology.org). webpage: aim provide coverage processes statistical tests, conditions application, information needed resulting statistical methods, probability distributions, variables, spread variation metrics. STATO also covers aspects experimental design description plots graphical representations commonly used provide visual cues data distribution layout assist review results. ontology object can set assigning ontology term identifier ontology slot struct_class object design time. ids can listed using $ notation: ontology method can used obtain detailed ontology information. cache = NULL struct package automatically attempt use OLS API (via rols package) obtain name description provided identifiers. used cached versions ontology definitions provided structToolbox package prevent issues connecting OLS API building package. Note ontology method returns definitions object (PCA) inputs/outputs (number_of_components).","code":"# create an example PCA object P=PCA()  # ontology for the PCA object P$ontology ## [1] \"OBI:0200051\" ontology(P,cache = ontology_cache()) # set cache = NULL (default) for online use ## [[1]] ## An object of class \"ontology_list\" ## Slot \"terms\": ## [[1]] ## term id:       OBI:0200051 ## ontology:      obi ## label:         principal components analysis dimensionality reduction ## description:   A principal components analysis dimensionality reduction is a dimensionality reduction ##                  achieved by applying principal components analysis and by keeping low-order principal ##                  components and excluding higher-order ones. ## iri:           http://purl.obolibrary.org/obo/OBI_0200051 ##  ##  ##  ## [[2]] ## An object of class \"ontology_list\" ## Slot \"terms\": ## [[1]] ## term id:       STATO:0000555 ## ontology:      stato ## label:         number of predictive components ## description:   number of predictive components is a count used as input to the principle component analysis ##                  (PCA) ## iri:           http://purl.obolibrary.org/obo/STATO_0000555"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"validating-supervised-statistical-models","dir":"Articles","previous_headings":"Introduction to struct objects, including models, model sequences, model charts and ontology.","what":"Validating supervised statistical models","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"Validation important aspect chemometric modelling. struct framework enables kind iterative model testing iterator objects.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"cross-validation","dir":"Articles","previous_headings":"Introduction to struct objects, including models, model sequences, model charts and ontology. > Validating supervised statistical models","what":"Cross-validation","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"Cross validation common technique assessing performance classification models. example use Partial least squares-discriminant analysis (PLS-DA) model. Data mean centred prior PLS, build model sequence first. iterator objects like k-fold cross-validation object (kfold_xval) can created just like struct object. Parameters can set creation using equals sign, accessed changed later using dollar notation. model cross-validated can set/accessed using models method. Alternatively, iterators can combined models using multiplication symbol shorthand models assignement method: run method can used iterator object. iterator run set model model sequence multiple times. case run cross-validation 5 times, splitting data different training test sets time. run method also needs metric specified, another type struct object. metric may calculated iterations, iteration, depending iterator type (resampling, permutation etc). cross-validation calculate “balanced accuracy” iterations. Note balanced_accuracy metric actually reports 1-accuracy, value 0 indicates perfect performance. standard deviation “sd” NA example one permutation. Like struct objects, iterators can chart objects associated . chart_names function list object. Charts iterator objects can plotted way charts object.  possible combine multiple iterators using multiplication symbol. equivalent nesting one iterator inside . example, can repeat cross-validation multiple times permuting sample order.","code":"M = mean_centre() + PLSDA(number_components=2,factor_name='class') M ## A model_seq object containing: ##  ## [1] ## A \"mean_centre\" object ## ---------------------- ## name:          Mean centre ## description:   The mean sample is subtracted from all samples in the data matrix. The features in the centred ##                  matrix all have zero mean. ## input params:  mode  ## outputs:       centred, mean_data, mean_sample_meta  ## predicted:     centred ## seq_in:        data ##  ## [2] ## A \"PLSDA\" object ## ---------------- ## name:          Partial least squares discriminant analysis ## description:   PLS is a multivariate regression technique that extracts latent variables maximising ##                  covariance between the input data and the response. The Discriminant Analysis ##                  variant uses group labels in the response variable. For >2 groups a 1-vs-all ##                  approach is used. Group membership can be predicted for test samples based on ##                  a probability estimate of group membership, or the estimated y-value. ## input params:  number_components, factor_name, pred_method  ## outputs:       scores, loadings, yhat, design_matrix, y, reg_coeff, probability, vip, pls_model, pred, threshold, sr, sr_pvalue  ## predicted:     pred ## seq_in:        data # create object XCV = kfold_xval(folds=5,factor_name='class')  # change the number of folds XCV$folds=10 XCV$folds ## [1] 10 models(XCV)=M models(XCV) ## A model_seq object containing: ##  ## [1] ## A \"mean_centre\" object ## ---------------------- ## name:          Mean centre ## description:   The mean sample is subtracted from all samples in the data matrix. The features in the centred ##                  matrix all have zero mean. ## input params:  mode  ## outputs:       centred, mean_data, mean_sample_meta  ## predicted:     centred ## seq_in:        data ##  ## [2] ## A \"PLSDA\" object ## ---------------- ## name:          Partial least squares discriminant analysis ## description:   PLS is a multivariate regression technique that extracts latent variables maximising ##                  covariance between the input data and the response. The Discriminant Analysis ##                  variant uses group labels in the response variable. For >2 groups a 1-vs-all ##                  approach is used. Group membership can be predicted for test samples based on ##                  a probability estimate of group membership, or the estimated y-value. ## input params:  number_components, factor_name, pred_method  ## outputs:       scores, loadings, yhat, design_matrix, y, reg_coeff, probability, vip, pls_model, pred, threshold, sr, sr_pvalue  ## predicted:     pred ## seq_in:        data # cross validation of a mean centred PLSDA model XCV = kfold_xval(         folds=5,         method='venetian',         factor_name='class') *        (mean_centre() + PLSDA(factor_name='class')) XCV = run(XCV,D,balanced_accuracy()) XCV$metric ##              metric mean sd ## 1 balanced_accuracy 0.11 NA chart_names(XCV) ## [1] \"kfoldxcv_grid\"   \"kfoldxcv_metric\" C = kfoldxcv_grid(   factor_name='class',   level=levels(D$sample_meta$class)[2]) # first level chart_plot(C,XCV) # permute sample order 10 times and run cross-validation P = permute_sample_order(number_of_permutations = 10) *      kfold_xval(folds=5,factor_name='class')*     (mean_centre() + PLSDA(factor_name='class',number_components=2)) P = run(P,D,balanced_accuracy()) P$metric ##              metric   mean          sd ## 1 balanced_accuracy 0.1095 0.004972145"},{"path":[]},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"introduction-2","dir":"Articles","previous_headings":"A typical workflow for processing and analysing mass spectrometry-based metabolomics data.","what":"Introduction","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"vignette provides overview structToolbox workflow implemented process (e.g. filter features, signal drift batch correction, normalise missing value imputation) mass spectrometry data. workflow exists methods part Peak Matrix Processing (pmp) package, including range additional filters described Kirwan et al., 2013, 2014. packages required vignette addition structToolbox:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"dataset-1","dir":"Articles","previous_headings":"A typical workflow for processing and analysing mass spectrometry-based metabolomics data.","what":"Dataset","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"demonstration purposes process analyse MTBLS79 dataset (‘Dataset 7:SFPM’ Kirwan et al., 2014. dataset represents systematic evaluation reproducibility multi-batch direct-infusion mass spectrometry (DIMS)-based metabolomics study cardiac tissue extracts. comprises twenty biological samples (cow vs. sheep) analysed repeatedly, 8 batches across 7 days, together concurrent set quality control (QC) samples. Data presented step data processing workflow available MetaboLights (https://www.ebi.ac.uk/metabolights/MTBLS79). MTBLS79_DatasetExperiment object included structToolbox package processed version MTBLS79 dataset available peak matrix processing (pmp) package. vignette describes step step structToolbox version created pmp version (.e. ‘Dataset 7:SFPM’ Scientific Data publication - https://doi.org/10.1038/sdata.2014.12). SummarizedExperiment object pmp package needs converted DatasetExperiment object use structToolbox. Full processing data set requires number steps. applied using single struct model sequence (model_seq).","code":"# the pmp SE object SE = MTBLS79  # convert to DE DE = as.DatasetExperiment(SE) DE$name = 'MTBLS79' DE$description = 'Converted from SE provided by the pmp package'  # add a column indicating the order the samples were measured in DE$sample_meta$run_order = 1:nrow(DE)  # add a column indicating if the sample is biological or a QC Type=as.character(DE$sample_meta$Class) Type[Type != 'QC'] = 'Sample' DE$sample_meta$Type = factor(Type)  # add a column for plotting batches DE$sample_meta$batch_qc = DE$sample_meta$Batch DE$sample_meta$batch_qc[DE$sample_meta$Type=='QC']='QC'  # convert to factors DE$sample_meta$Batch = factor(DE$sample_meta$Batch) DE$sample_meta$Type = factor(DE$sample_meta$Type) DE$sample_meta$Class = factor(DE$sample_meta$Class) DE$sample_meta$batch_qc = factor(DE$sample_meta$batch_qc)  # print summary DE ## A \"DatasetExperiment\" object ## ---------------------------- ## name:          MTBLS79 ## description:   Converted from SE provided by the pmp package ## data:          172 rows x 2488 columns ## sample_meta:   172 rows x 7 columns ## variable_meta: 2488 rows x 0 columns"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"signal-drift-and-batch-correction","dir":"Articles","previous_headings":"A typical workflow for processing and analysing mass spectrometry-based metabolomics data.","what":"Signal drift and batch correction","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"batch correction algorithm applied reduce intra- inter- batch variations dataset. Quality Control-Robust Spline Correction (QC-RSC) provided pmp package, wrapped structToolbox object called sb_corr. figure shows plot feature vs run order, correction. fitted spline batch shown grey. can seen correction removed instrument drift within batches.   additional step added published workflow remove feature corrected QCRCMS. can occur enough measured QC values within batch. QCRMS pmp package currently returns NA samples feature occurs. Features occurs excluded. output step output MTBLS79_DatasetExperiment(filtered=FALSE).","code":"M = # batch correction     sb_corr(       order_col='run_order',       batch_col='Batch',        qc_col='Type',        qc_label='QC',       spar_lim = c(0.6,0.8)      )  M = model_apply(M,DE) C = feature_profile(       run_order='run_order',       qc_label='QC',       qc_column='Type',       colour_by='batch_qc',       feature_to_plot='200.03196',       plot_sd=FALSE   )  # plot and modify using ggplot2  chart_plot(C,M,DE)+ylab('Peak area')+ggtitle('Before') chart_plot(C,predicted(M))+ylab('Peak area')+ggtitle('After') M2 = filter_na_count(       threshold=3,       factor_name='Batch'     ) M2 = model_apply(M2,predicted(M))  # calculate number of features removed nc = ncol(DE) - ncol(predicted(M2))  cat(paste0('Number of features removed: ', nc)) ## Number of features removed: 425"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"feature-filtering","dir":"Articles","previous_headings":"A typical workflow for processing and analysing mass spectrometry-based metabolomics data.","what":"Feature filtering","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"journal article three spectral cleaning steps applied. first filter Kruskal-Wallis test used identify features reliably detected QC samples (p < 0.0001) batches. follow parameters original article use multiple test correction (mtc = 'none'). make use univariate tests kw_rank_sum filter advanced features struct needed. Slots predicted, seq_in used ensure correct output univariate test connected correct input feature filter using filter_by_name. Another slot seq_fcn used extract relevant column predicted output compatible seq_in input. placeholder used “names” parameter (names = 'place_holder') input replaced output seq_fcn. second filter Wilcoxon Signed-Rank test. used identify features representative average biological samples (p < 1e-14). make use seq_in seq_fcn. Finally, RSD filter used remove features high analytical variation (QC RSD > 20 removed) output filter output MTBLS79_DatasetExperiment(filtered=TRUE).","code":"M3 = kw_rank_sum(       alpha=0.0001,       mtc='none',       factor_names='Batch',       predicted='significant'     ) +     filter_by_name(       mode='exclude',       dimension = 'variable',       seq_in = 'names',        names='seq_fcn', # this is a placeholder and will be replaced by seq_fcn       seq_fcn=function(x){return(x[,1])}     ) M3 = model_apply(M3, predicted(M2))  nc = ncol(predicted(M2)) - ncol(predicted(M3)) cat(paste0('Number of features removed: ', nc)) ## Number of features removed: 262 M4 = wilcox_test(       alpha=1e-14,       factor_names='Type',        mtc='none',        predicted = 'significant'     ) +     filter_by_name(       mode='exclude',       dimension='variable',       seq_in='names',        names='place_holder',       seq_fcn=function(x){return(x$significant)}     ) M4 = model_apply(M4, predicted(M3))  nc = ncol(predicted(M3)) - ncol(predicted(M4)) cat(paste0('Number of features removed: ', nc)) ## Number of features removed: 169 M5 = rsd_filter(      rsd_threshold=20,      factor_name='Type' ) M5 = model_apply(M5,predicted(M4))  nc = ncol(predicted(M4)) - ncol(predicted(M5)) cat(paste0('Number of features removed: ', nc)) ## Number of features removed: 53"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"normalisation-missing-value-imputation-and-scaling","dir":"Articles","previous_headings":"A typical workflow for processing and analysing mass spectrometry-based metabolomics data.","what":"Normalisation, missing value imputation and scaling","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"apply number common pre-processing steps filtered peak matrix identical steps applied described Kirwan et al. 2013, 2014. Probabilistic Quotient Normalisation (PQN) k-nearest neighbours imputation (k = 5) Generalised log transform (glog) steps prepare data multivariate analysis accounting sample concentration differences, imputing missing values scaling data.","code":"# peak matrix processing M6 = pqn_norm(qc_label='QC',factor_name='Type') +       knn_impute(neighbours=5) +      glog_transform(qc_label='QC',factor_name='Type') M6 = model_apply(M6,predicted(M5))"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"exploratory-analysis","dir":"Articles","previous_headings":"A typical workflow for processing and analysing mass spectrometry-based metabolomics data.","what":"Exploratory Analysis","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"Principal Component Analysis (PCA) can used visualise high-dimensional data. unsupervised method maximises variance reduced number latent variables, principal components.  plot similar Figure 3b original publication link. Sample replicates represented colours samples groups (C = cow S = Sheep) different shapes. Plotting scores colouring Batch indicates signal/batch correction effective batches overlapping.","code":"# PCA M7  = mean_centre() + PCA(number_components = 2)  # apply model sequence to data M7 = model_apply(M7,predicted(M6))  # plot pca scores C = pca_scores_plot(factor_name=c('Sample_Rep','Class'),ellipse='none') chart_plot(C,M7[2]) + coord_fixed() +guides(colour=FALSE) ## Warning: The `<scale>` argument of `guides()` cannot be `FALSE`. Use \"none\" instead as ## of ggplot2 3.3.4. ## This warning is displayed once every 8 hours. ## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was ## generated. # chart object C = pca_scores_plot(factor_name=c('Batch'),ellipse='none') # plot chart_plot(C,M7[2]) + coord_fixed()"},{"path":[]},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"introduction-3","dir":"Articles","previous_headings":"Partial Least Squares (PLS) analysis of a untargeted LC-MS-based clinical metabolomics dataset.","what":"Introduction","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"aim vignette demonstrate 1) apply validate Partial Least Squares (PLS) analysis using structToolbox, 2) reproduce statistical analysis Thevenot et al. (2015) 3. compare different implementations PLS.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"dataset-2","dir":"Articles","previous_headings":"Partial Least Squares (PLS) analysis of a untargeted LC-MS-based clinical metabolomics dataset.","what":"Dataset","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"objective original study : > …study influence age, body mass index (bmi), gender metabolite concentrations urine, analysing 183 samples cohort adults liquid chromatography coupled high-resolution mass spectrometry. Thevenot et al. (2015) “Sacurine” dataset needs converted DatasetExperiment object. ropls package provides data list containing dataMatrix, sampleMetadata variableMetadata.","code":"data('sacurine',package = 'ropls') # the 'sacurine' list should now be available  # move the annotations to a new column and rename the features by index to avoid issues # later when data.frames get transposed and names get checked/changed sacurine$variableMetadata$annotation=rownames(sacurine$variableMetadata) rownames(sacurine$variableMetadata)=1:nrow(sacurine$variableMetadata) colnames(sacurine$dataMatrix)=1:ncol(sacurine$dataMatrix)  # create DatasetExperiment DE = DatasetExperiment(data = data.frame(sacurine$dataMatrix),                        sample_meta = sacurine$sampleMetadata,                        variable_meta = sacurine$variableMetadata,                        name = 'Sacurine data',                        description = 'See ropls package documentation for details')  # print summary DE ## A \"DatasetExperiment\" object ## ---------------------------- ## name:          Sacurine data ## description:   See ropls package documentation for details ## data:          183 rows x 109 columns ## sample_meta:   183 rows x 3 columns ## variable_meta: 109 rows x 4 columns"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"data-preprocessing","dir":"Articles","previous_headings":"Partial Least Squares (PLS) analysis of a untargeted LC-MS-based clinical metabolomics dataset.","what":"Data preprocessing","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"Sacurine dataset used within vignette already pre-processed: signal drift batch effect correction intensities, urine profile normalized osmolality sample. Finally, data log10 transformed.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"exploratory-data-analysis","dir":"Articles","previous_headings":"Partial Least Squares (PLS) analysis of a untargeted LC-MS-based clinical metabolomics dataset.","what":"Exploratory data analysis","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"Since data already processed data can visualised using Principal Component Analysis (PCA) without pre-processing. ropls package automatically applies unit variance scaling (autoscaling) default. approach applied .  third plot coloured gender (C) identical Figure 2 ropls package vignette. structToolbox package provides range PCA-related diagnostic plots, including D-statistic, scree, loadings plots. plots can used explore variance data.","code":"# prepare model sequence M = autoscale() + PCA(number_components = 5) # apply model sequence to dataset M = model_apply(M,DE)  # pca scores plots g=list() for (k in colnames(DE$sample_meta)) {     C = pca_scores_plot(factor_name = k)     g[[k]] = chart_plot(C,M[2]) } # plot using cowplot plot_grid(plotlist=g, nrow=1, align='vh', labels=c('A','B','C')) C = pca_scree_plot() g1 = chart_plot(C,M[2])  C = pca_loadings_plot() g2 = chart_plot(C,M[2])  C = pca_dstat_plot(alpha=0.95) g3 = chart_plot(C,M[2])  p1=plot_grid(plotlist = list(g1,g2),align='h',nrow=1,axis='b') p2=plot_grid(plotlist = list(g3),nrow=1) plot_grid(p1,p2,nrow=2)"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"partial-least-squares-pls-analysis","dir":"Articles","previous_headings":"Partial Least Squares (PLS) analysis of a untargeted LC-MS-based clinical metabolomics dataset.","what":"Partial Least Squares (PLS) analysis","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"ropls package uses implementation (O)PLS algorithms. structToolbox uses pls package, interesting compare outputs approaches. simplicity scores plots compared.  plot similar fig.3 ropls vignette. Differences due inverted LV axes, common occurrence NIPALS algorithm (used structToolbox ropls) depends algorithm initialised. compare R2 values model structToolbox use regression model, instead discriminant model. convert gender factor numeric variable applying model.  ropls package automatically applies cross-validation asses performance PLSDA model. structToolbox applied separately give control approach used desired. default cross-validation used ropls package 7-fold cross-validation replicate . Training set R2: 0.6975706 0.6798415 0.646671 0.6532914 0.7109769 0.670777 0.6935344 Test set Q2: 0.5460723 validity model can assessed using permutation testing. return discriminant model.  permuted models balanced accuracy around 50%, expected dataset two groups. unpermuted models balanced accuracy around 90% therefore much better might expected occur chance.","code":"# prepare model sequence M = autoscale() + PLSDA(factor_name='gender') M = model_apply(M,DE)  C = pls_scores_plot(factor_name = 'gender') chart_plot(C,M[2]) # convert gender to numeric DE$sample_meta$gender=as.numeric(DE$sample_meta$gender)  # models sequence M = autoscale(mode='both') + PLSR(factor_name='gender',number_components=3) M = model_apply(M,DE)  # some diagnostic charts C = plsr_cook_dist() g1 = chart_plot(C,M[2])  C = plsr_prediction_plot() g2 = chart_plot(C,M[2])  C = plsr_qq_plot() g3 = chart_plot(C,M[2])  C = plsr_residual_hist() g4 = chart_plot(C,M[2])  plot_grid(plotlist = list(g1,g2,g3,g4), nrow=2,align='vh') # model sequence M = kfold_xval(folds=7, factor_name='gender') *      (autoscale(mode='both') + PLSR(factor_name='gender')) M = run(M,DE,r_squared()) # reset gender to original factor DE$sample_meta$gender=sacurine$sampleMetadata$gender # model sequence M = permutation_test(number_of_permutations = 10, factor_name='gender') *     kfold_xval(folds=7,factor_name='gender') *     (autoscale() + PLSDA(factor_name='gender',number_components = 3)) M = run(M,DE,balanced_accuracy())  C = permutation_test_plot(style='boxplot') chart_plot(C,M)+ylab('1 - balanced accuracy')"},{"path":[]},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"introduction-4","dir":"Articles","previous_headings":"Univariate and multivariate statistical analysis of a NMR-based clinical metabolomics dataset.","what":"Introduction","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"purpose vignette demonstrate different functionalities methods available part structToolbox reproduce data analysis reported Mendez et al., (2020) Chan et al., (2016).","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"dataset-3","dir":"Articles","previous_headings":"Univariate and multivariate statistical analysis of a NMR-based clinical metabolomics dataset.","what":"Dataset","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"1H-NMR dataset used described Mendez et al., (2020) vignette contains processed spectra urine samples obtained gastric cancer healthy patients Chan et al., (2016). experimental raw data available Metabolomics Workbench (PR000699) processed version available Excel data file. first step need reorganise convert Excel data file DatasetExperiment object. Using openxlsx package file can read directly R data.frame manipulated required.","code":"url = 'https://github.com/CIMCB/MetabWorkflowTutorial/raw/master/GastricCancer_NMR.xlsx'  # read in file directly from github... # X=read.xlsx(url)  # ...or use BiocFileCache path = bfcrpath(bfc,url) X = read.xlsx(path)  # sample meta data SM=X[,1:4] rownames(SM)=SM$SampleID # convert to factors SM$SampleType=factor(SM$SampleType) SM$Class=factor(SM$Class) # keep a numeric version of class for regression SM$Class_num = as.numeric(SM$Class)  ## data matrix # remove meta data X[,1:4]=NULL rownames(X)=SM$SampleID  # feature meta data VM=data.frame(idx=1:ncol(X)) rownames(VM)=colnames(X)  # prepare DatasetExperiment DE = DatasetExperiment(     data=X,     sample_meta=SM,     variable_meta=VM,     description='1H-NMR urinary metabolomic profiling for diagnosis of gastric cancer',     name='Gastric cancer (NMR)')  DE ## A \"DatasetExperiment\" object ## ---------------------------- ## name:          Gastric cancer (NMR) ## description:   1H-NMR urinary metabolomic profiling for diagnosis of gastric cancer ## data:          140 rows x 149 columns ## sample_meta:   140 rows x 5 columns ## variable_meta: 149 rows x 1 columns"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"data-pre-processing-and-quality-assessment","dir":"Articles","previous_headings":"Univariate and multivariate statistical analysis of a NMR-based clinical metabolomics dataset.","what":"Data pre-processing and quality assessment","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"good practice remove features may low quality, assess quality data general. Tutorial features QC-RSD > 20% 10% features missing retained. Note additional feature vs processing reported Mendez et. al. filters use >= <= instead > <. suitable scaling transformation PCA can used assess data quality. expected biological variance (samples) larger technical variance (QCs). workflow reproducing (link) following steps applied: log10 transform autoscaling (scaled unit variance) knn imputation (3 neighbours) transformed scaled matrix used input PCA. Using struct can chain steps single model sequence.","code":"# prepare model sequence M = rsd_filter(rsd_threshold=20,qc_label='QC',factor_name='Class') +     mv_feature_filter(threshold = 10,method='across',factor_name='Class')  # apply model M = model_apply(M,DE)  # get the model output filtered = predicted(M)  # summary of filtered data filtered ## A \"DatasetExperiment\" object ## ---------------------------- ## name:          Gastric cancer (NMR) ## description:   1H-NMR urinary metabolomic profiling for diagnosis of gastric cancer ## data:          140 rows x 53 columns ## sample_meta:   140 rows x 5 columns ## variable_meta: 53 rows x 1 columns # prepare the model sequence M = log_transform(base = 10) +     autoscale() +      knn_impute(neighbours = 3) +     PCA(number_components = 10)  # apply model sequence to data M = model_apply(M,filtered)  # get the transformed, scaled and imputed matrix TSI = predicted(M[3])  # scores plot C = pca_scores_plot(factor_name = 'SampleType') g1 = chart_plot(C,M[4])  # loadings plot C = pca_loadings_plot() g2 = chart_plot(C,M[4])  plot_grid(g1,g2,align='hv',nrow=1,axis='tblr')"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"univariate-statistics","dir":"Articles","previous_headings":"Univariate and multivariate statistical analysis of a NMR-based clinical metabolomics dataset.","what":"Univariate statistics","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"structToolbox provides number objects ttest, counting numbers features etc. brevity ttest calculated comparison workflow following (link). QC samples need excluded, data reduced GC groups.","code":"# prepare model TT = filter_smeta(mode='include',factor_name='Class',levels=c('GC','HE')) +        ttest(alpha=0.05,mtc='fdr',factor_names='Class')  # apply model TT = model_apply(TT,filtered)  # keep the data filtered by group for later filtered = predicted(TT[1])  # convert to data frame out=as_data_frame(TT[2])  # show first few features head(out) ##     t_statistic   t_p_value t_significant estimate.mean.GC estimate.mean.HE ## M4   -3.5392652 0.008421042          TRUE         26.47778         51.73947 ## M5    1.4296604 0.410396437         FALSE        265.11860        169.91500 ## M7    2.7456506 0.051494976         FALSE        118.52558         53.98718 ## M8   -2.1294198 0.178392032         FALSE         54.39535         79.26750 ## M11   0.5106536 0.776939682         FALSE        201.34390        171.27949 ## M14  -1.4786810 0.403091881         FALSE         61.53171         83.90250 ##         lower      upper ## M4  -39.56162 -10.961769 ## M5  -38.04747 228.454679 ## M7   17.60818 111.468619 ## M8  -48.20069  -1.543611 ## M11 -87.30604 147.434869 ## M14 -52.57754   7.835950"},{"path":[]},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"training-and-test-sets","dir":"Articles","previous_headings":"Univariate and multivariate statistical analysis of a NMR-based clinical metabolomics dataset. > Multivariate statistics and machine learning","what":"Training and Test sets","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"Splitting data training test sets important aspect machine learning. structToolbox implemented using split_data object random subsampling across whole dataset, stratified_split splitting based group sizes, approach used Mendez et al.","code":"# prepare model M = stratified_split(p_train=0.75,factor_name='Class') # apply to filtered data M = model_apply(M,filtered) # get data from object train = M$training train ## A \"DatasetExperiment\" object ## ---------------------------- ## name:          Gastric cancer (NMR) ##                (Training set) ## description:   • 1H-NMR urinary metabolomic profiling for diagnosis of gastric cancer ##                • A subset of the data has been selected as a training set ## data:          62 rows x 53 columns ## sample_meta:   62 rows x 5 columns ## variable_meta: 53 rows x 1 columns cat('\\n') test = M$testing test ## A \"DatasetExperiment\" object ## ---------------------------- ## name:          Gastric cancer (NMR) ##                (Testing set) ## description:   • 1H-NMR urinary metabolomic profiling for diagnosis of gastric cancer ##                • A subset of the data has been selected as a test set ## data:          21 rows x 53 columns ## sample_meta:   21 rows x 5 columns ## variable_meta: 53 rows x 1 columns"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"optimal-number-of-pls-components","dir":"Articles","previous_headings":"Univariate and multivariate statistical analysis of a NMR-based clinical metabolomics dataset. > Multivariate statistics and machine learning","what":"Optimal number of PLS components","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"Mendez et al k-fold cross-validation used determine optimal number PLS components. 100 bootstrap iterations used generate confidence intervals. strucToolbox implemented using “iterator” objects, can combined model objects. R2 used metric optimisation, PLSR model structToolbox used. speed 10 bootstrap iterations used .  chart plotted shows Q2, comparable Figure 13 Mendez et al . Two components selected Mendez et al, use .","code":"# scale/transform training data M = log_transform(base = 10) +     autoscale() +      knn_impute(neighbours = 3,by='samples')  # apply model M = model_apply(M,train)  # get scaled/transformed training data train_st = predicted(M)  # prepare model sequence MS = grid_search_1d(         param_to_optimise = 'number_components',         search_values = as.numeric(c(1:6)),         model_index = 2,         factor_name = 'Class_num',         max_min = 'max') *      permute_sample_order(         number_of_permutations = 10) *      kfold_xval(         folds = 5,         factor_name = 'Class_num') *       (mean_centre(mode='sample_meta')+       PLSR(factor_name='Class_num'))  # run the validation MS = struct::run(MS,train_st,r_squared())  # C = gs_line() chart_plot(C,MS)"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"pls-model-evalutation","dir":"Articles","previous_headings":"Univariate and multivariate statistical analysis of a NMR-based clinical metabolomics dataset. > Multivariate statistics and machine learning","what":"PLS model evalutation","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"evaluate model discriminant analysis structToolbox PLSDA model appropriate.  Note default cutoff B figure PLS models structToolbox 0, groups encoded +/-1. impact overall performance model.","code":"# prepare the discriminant model P = PLSDA(number_components = 2, factor_name='Class')  # apply the model P = model_apply(P,train_st)  # charts C = plsda_predicted_plot(factor_name='Class',style='boxplot') g1 = chart_plot(C,P)  C = plsda_predicted_plot(factor_name='Class',style='density') g2 = chart_plot(C,P)+xlim(c(-2,2))  C = plsda_roc_plot(factor_name='Class') g3 = chart_plot(C,P)  plot_grid(g1,g2,g3,align='vh',axis='tblr',nrow=1, labels=c('A','B','C')) # AUC for comparison with Mendez et al MET = calculate(AUC(),P$y$Class,P$yhat[,1]) MET ## A \"AUC\" object ## -------------- ## name:          Area under ROC curve ## description:   The area under the ROC curve of a classifier is estimated using the trapezoid method. ## value:         0.9739583"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"permutation-test","dir":"Articles","previous_headings":"Univariate and multivariate statistical analysis of a NMR-based clinical metabolomics dataset. > Multivariate statistics and machine learning","what":"Permutation test","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"permutation test can used assess likely observed result occurred chance. structToolbox permutation_test iterator object can combined iterators models.  plot comparable bottom half Figure 17 Mendez et. al.. unpermuted (true) Q2 values consistently better permuted (null) models. .e. model reliable.","code":"# model sequence MS = permutation_test(number_of_permutations = 20,factor_name = 'Class_num') *       kfold_xval(folds = 5,factor_name = 'Class_num') *      (mean_centre(mode='sample_meta') + PLSR(factor_name='Class_num', number_components = 2))  # run iterator MS = struct::run(MS,train_st,r_squared())  # chart C = permutation_test_plot(style = 'density')  chart_plot(C,MS) + xlim(c(-1,1)) + xlab('R Squared')"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"pls-projection-plots","dir":"Articles","previous_headings":"Univariate and multivariate statistical analysis of a NMR-based clinical metabolomics dataset. > Multivariate statistics and machine learning","what":"PLS projection plots","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"PLS can also used visualise model interpret latent variables.","code":"# prepare the discriminant model P = PLSDA(number_components = 2, factor_name='Class')  # apply the model P = model_apply(P,train_st)  C = pls_scores_plot(components=c(1,2),factor_name = 'Class') chart_plot(C,P)"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"pls-feature-importance","dir":"Articles","previous_headings":"Univariate and multivariate statistical analysis of a NMR-based clinical metabolomics dataset. > Multivariate statistics and machine learning","what":"PLS feature importance","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"Regression coefficients VIP scores can used estimate importance individual features PLS model. Mendez et al bootstrapping used estimate confidence intervals, brevity skip .","code":"# prepare chart C = pls_vip_plot(ycol = 'HE') g1 = chart_plot(C,P)  C = pls_regcoeff_plot(ycol='HE') g2 = chart_plot(C,P)  plot_grid(g1,g2,align='hv',axis='tblr',nrow=2)"},{"path":[]},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"introduction-5","dir":"Articles","previous_headings":"Classification of Metabolomics Data using Support Vector Machines.","what":"Introduction","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"aim vignette illustrate apply SVM analysis Classifying Metabolomics data. Support vector Machines (SVM) commonly used method Machine Learning. classification tasks used generate boundary groups samples training set. well generating linear boundaries, SVM can extended exploit use kernels generate complex non-linear boundaries groups required. structToolbox package, SVM functionality provided e1071 package incorporated model object. chart object (svm_plot_2d) also available plot SVM boundaries data two variables.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"dataset-4","dir":"Articles","previous_headings":"Classification of Metabolomics Data using Support Vector Machines.","what":"Dataset","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"1H-NMR dataset used described Mendez et al., (2020) vignette contains processed spectra urine samples obtained gastric cancer healthy patients Chan et al., (2016). raw experimental data available Metabolomics Workbench (PR000699) processed version available Excel data file. simplicity use pre-processed version 1H-NMR “Gastric cancer” dataset using structToolbox package. Details regards pre-processing reported “NMR_clinical_metabolomics” vignette `r Biocpkg(“structToolbox”) package. purposes illustrating effect SVM parameters boundary groups, reduce data include GC groups apply PLS reduce data two components. treat PLS scores two group dataset two features.","code":"# summary of DatasetExperiment object DE ## A \"DatasetExperiment\" object ## ---------------------------- ## name:           ## description:    ## data:          140 rows x 53 columns ## sample_meta:   140 rows x 5 columns ## variable_meta: 53 rows x 1 columns # model sequence and pls model (NB data already centred) MS = filter_smeta(mode = 'include', levels = c('GC','HE'), factor_name = 'Class') +      PLSDA(factor_name = 'Class',number_components = 2)  # apply PLS model MS = model_apply(MS,DE)  # plot the data C = pls_scores_plot(factor_name = 'Class') chart_plot(C,MS[2]) # new DatasetExperiment object from the PLS scores DE2 = DatasetExperiment(   data = MS[2]$scores$data,    sample_meta = predicted(MS[1])$sample_meta,   variable_meta = data.frame('LV'=c(1,2),row.names = colnames(MS[2]$scores)),   name = 'Illustrativate SVM dataset',   description = 'Generated by applying PLS to the processed Gastric cancer (NMR) dataset' )  DE2 ## A \"DatasetExperiment\" object ## ---------------------------- ## name:          Illustrativate SVM dataset ## description:   Generated by applying PLS to the processed Gastric cancer (NMR) dataset ## data:          83 rows x 2 columns ## sample_meta:   83 rows x 5 columns ## variable_meta: 2 rows x 1 columns"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"basic-svm-model","dir":"Articles","previous_headings":"Classification of Metabolomics Data using Support Vector Machines.","what":"Basic SVM model","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"simplest SVM model uses linear kernel. structToolbox SVM model can used train apply SVM models. svm_plot_2d chart object provided visualisation boundaries two dimensions.  SVM boundary plotted black, margins grey support vectors indicated grey circles.","code":"# SVM model M = SVM(   factor_name = 'Class',   kernel = 'linear' )  # apply model M = model_apply(M,DE2)  # plot boundary C = svm_plot_2d(factor_name = 'Class') chart_plot(C,M, DE2)"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"svm-cost-function","dir":"Articles","previous_headings":"Classification of Metabolomics Data using Support Vector Machines.","what":"SVM cost function","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"SVM cost function applies penalty samples wrong side margins. high penalty results narrow margin tries force samples correct side boundary. low penalty makes wider margin less strict samples misclassified. optimal cost use data dependent.","code":"# low cost M$cost=0.01 M=model_apply(M,DE2) C=svm_plot_2d(factor_name='Species') g1=chart_plot(C,M,DE2)  # medium cost M$cost=0.05 M=model_apply(M,DE2) C=svm_plot_2d(factor_name='Species') g2=chart_plot(C,M,DE2)  # high cost M$cost=100 M=model_apply(M,DE2)  C=svm_plot_2d(factor_name='Species') g3=chart_plot(C,M,DE2)  # plot prow <- plot_grid(   g1 + theme(legend.position=\"none\"),   g2 + theme(legend.position=\"none\"),   g3 + theme(legend.position=\"none\"),   align = 'vh',   labels = c(\"Low cost\", \"Medium cost\", \"High cost\"),   hjust = -1,   nrow = 2 )  legend <- get_legend(   # create some space to the left of the legend   g1 + guides(color = guide_legend(nrow = 1)) +   theme(legend.position = \"bottom\") )  plot_grid(prow, legend, ncol=1, rel_heights = c(1, .1))"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"kernel-functions","dir":"Articles","previous_headings":"Classification of Metabolomics Data using Support Vector Machines.","what":"Kernel functions","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"number different kernels can used support vector machines. structToolbox wrapper ‘linear’, ‘polynomial’,‘radial’ ‘sigmoid’ kernels can specified. Using kernels allows boundary flexible, often require additional parameters specified. best kernel use vary depending dataset, common choice radial kernel allows high flexibility single parameter.  parameters kernel can used control complexity boundary. show radial kernel parameter “gamma” can used change complexity boundary. combination cost parameter (keep constant ) allows highly flexible boundary models.  Note best practice select optimal kernel parameter(s) combination cost parameter (e.g. 2d grid search) best combination identified.","code":"# set a fixed cost for this comparison M$cost=1  # linear kernel M$kernel='linear' M=model_apply(M,DE2) C=svm_plot_2d(factor_name='Species') g1=chart_plot(C,M,DE2)  # polynomial kernel M$kernel='polynomial' M$gamma=1 M$coef0=0 M=model_apply(M,DE2) C=svm_plot_2d(factor_name='Species') g2=chart_plot(C,M,DE2)  # rbf kernel M$kernel='radial' M$gamma=1 M=model_apply(M,DE2) C=svm_plot_2d(factor_name='Species') g3=chart_plot(C,M,DE2)  # sigmoid kernel M$kernel='sigmoid' M$gamma=1 M$coef0=0 M=model_apply(M,DE2) C=svm_plot_2d(factor_name='Species') g4=chart_plot(C,M,DE2)  # plot prow <- plot_grid(   g1 + theme(legend.position=\"none\"),   g2 + theme(legend.position=\"none\"),   g3 + theme(legend.position=\"none\"),   g4 + theme(legend.position=\"none\"),   align = 'vh',   labels = c(\"Linear\", \"Polynomial\", \"Radial\",\"Sigmoid\"),   hjust = -1,   nrow = 2 ) legend <- get_legend(   # create some space to the left of the legend   g1 + guides(color = guide_legend(nrow = 1)) +     theme(legend.position = \"bottom\") ) plot_grid(prow, legend, ncol = 1, rel_heights = c(1, .1)) # rbf kernel and cost M$kernel = 'radial' M$cost = 1  # low gamma M$gamma=0.01 M=model_apply(M,DE2) C=svm_plot_2d(factor_name='Species') g1=chart_plot(C,M,DE2)  # medium gamma M$gamma=0.1 M=model_apply(M,DE2) C=svm_plot_2d(factor_name='Species') g2=chart_plot(C,M,DE2)  # high gamma M$gamma=1 M=model_apply(M,DE2) C=svm_plot_2d(factor_name='Species') g3=chart_plot(C,M,DE2)  # plot prow <- plot_grid(  g1 + theme(legend.position=\"none\"),  g2 + theme(legend.position=\"none\"),  g3 + theme(legend.position=\"none\"),  align = 'vh',  labels = c(\"Low gamma\", \"Medium gamma\", \"High gamma\"),  hjust = -1,  nrow = 2 ) legend <- get_legend(   # create some space to the left of the legend   g1 + guides(color = guide_legend(nrow = 1)) +   theme(legend.position = \"bottom\") ) plot_grid(prow, legend, ncol = 1, rel_heights = c(1, .1))"},{"path":[]},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"introduction-6","dir":"Articles","previous_headings":"Exploratory data analysis of LC-MS-based proteomics and metabolomics datasets (STATegra project)","what":"Introduction","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"aim vignette conduct data preprocessing exploratory analysis data STATegra project (https://www.nature.com/articles/s41597-019-0202-7). demonstration purposes focus Proteomics Metabolomics datasets publicly available part STATegra multi-omics dataset. …STATegra multi-omics dataset combines measurements 10 different omics technologies applied biological system, namely well-studied mouse pre-B-cell differentiation. STATegra includes high-throughput measurements chromatin structure, gene expression, proteomics metabolomics, complemented single-cell data. Gomez-Cabrero et al STATegra includes high-throughput measurements chromatin structure, gene expression, proteomics metabolomics, complemented single-cell data.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"lc-ms-based-proteomics-dataset","dir":"Articles","previous_headings":"Exploratory data analysis of LC-MS-based proteomics and metabolomics datasets (STATegra project)","what":"LC-MS-based proteomics dataset","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"LC-MS-based proteomics dataset STATegra multi-omics dataset (see Introduction) can found github must extracted zip file prior data analysis. imported data needs converted DatasetExperiment format use structToolbox. number Reporter genes included study. plot two illustrate trends data.","code":"# path to zip zipfile = \"https://raw.github.com/STATegraData/STATegraData/master/Script_STATegra_Proteomics.zip\"  ## retrieve from BiocFileCache path = bfcrpath(bfc,zipfile) temp = bfccache(bfc)  ## ... or download to temp location # path = tempfile() # temp = tempdir() # download.file(zipfile,path)  # unzip unzip(path, files = \"Proteomics_01_uniprot_canonical_normalized.txt\", exdir=temp) # read samples all_data <-  read.delim(file.path(temp,\"Proteomics_01_uniprot_canonical_normalized.txt\"), as.is = TRUE, header = TRUE, sep = \"\\t\") # extract data matrix data = all_data[1:2527,51:86] # shorten sample names colnames(data) = lapply(colnames(data), function (x) substr(x, 27, nchar(x))) # replace 0 with NA data[data == 0] <- NA # transpose data=as.data.frame(t(data))  # prepare sample meta SM = lapply(rownames(data),function(x) {   s=strsplit(x,'_')[[1]] # split at underscore   out=data.frame(     'treatment' = s[[1]],     'time' = substr(s[[2]],1,nchar(s[[2]])-1) ,         'batch' = substr(s[[3]],6,nchar(s[[3]])),     'condition' = substr(x,1,6) # interaction between treatment and time   )   return(out) }) SM = do.call(rbind,SM) rownames(SM)=rownames(data) # convert to factors SM$treatment=factor(SM$treatment) SM$time=ordered(SM$time,c(\"0\",\"2\",\"6\",\"12\",\"18\",\"24\")) SM$batch=ordered(SM$batch,c(1,3,4,5,6,7)) SM$condition=factor(SM$condition)  # variable meta data VM = all_data[1:2527,c(1,6,7)] rownames(VM)=colnames(data)  # prepare DatasetExperiment DS = DatasetExperiment(   data = data,    sample_meta = SM,    variable_meta = VM,    name = 'STATegra Proteomics',   description = 'downloaded from: https://github.com/STATegraData/STATegraData/' ) DS ## A \"DatasetExperiment\" object ## ---------------------------- ## name:          STATegra Proteomics ## description:   downloaded from: https://github.com/STATegraData/STATegraData/ ## data:          36 rows x 2527 columns ## sample_meta:   36 rows x 4 columns ## variable_meta: 2527 rows x 3 columns # find id of reporters Ldha = which(DS$variable_meta$Gene.names=='Ldha') Hk2 = which(DS$variable_meta$Gene.names=='Hk2')  # chart object C = feature_boxplot(feature_to_plot=Ldha,factor_name='time',label_outliers=FALSE) g1=chart_plot(C,DS)+ggtitle('Ldha')+ylab('expression') C = feature_boxplot(feature_to_plot=Hk2,factor_name='time',label_outliers=FALSE) g2=chart_plot(C,DS)+ggtitle('Hk2')+ylab('expression')  plot_grid(g1,g2,nrow=1,align='vh',axis='tblr')"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"data-transformation","dir":"Articles","previous_headings":"Exploratory data analysis of LC-MS-based proteomics and metabolomics datasets (STATegra project) > LC-MS-based proteomics dataset","what":"Data transformation","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"data log2 transformed, scaled mean medians equal conditions. steps available structToolbox using log_transform mean_of_medians objects. Reporter genes plotted comparison.","code":"# prepare model sequence M = log_transform(   base=2) +    mean_of_medians(     factor_name = 'condition') # apply model sequence M = model_apply(M,DS)   # get transformed data DST = predicted(M) # chart object C = feature_boxplot(feature_to_plot=Ldha,factor_name='time',label_outliers=FALSE) g1=chart_plot(C,DST)+ggtitle('Ldha')+ylab('log2(expression)') C = feature_boxplot(feature_to_plot=Hk2,factor_name='time',label_outliers=FALSE) g2=chart_plot(C,DST)+ggtitle('Hk2')+ylab('log2(expression)')  plot_grid(g1,g2,nrow=1,align='vh',axis='tblr')"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"missing-value-filtering","dir":"Articles","previous_headings":"Exploratory data analysis of LC-MS-based proteomics and metabolomics datasets (STATegra project) > LC-MS-based proteomics dataset","what":"Missing value filtering","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"Missing value filtering involves removing feature (gene) least 3 missing values per group least 11 samples. specific filter structToolbox time, can achieved combining filter_na_count filter_by_name objects. Specifically, default output filter_na_count changed return matrix NA counts per class. output connected ‘names’ input filter_by_names converted TRUE/FALSE using ‘seq_fcn’ input. ‘seq_fcn’ function processes NA counts used inputs filter_by_names. data passed along model sequence passes unchanged filter_na_count object becuase default output changed, filter_na_count filter_by_name objects working together single filter.","code":"# build model sequence M2 = filter_na_count(   threshold=2,   factor_name='condition',    predicted='na_count') +    # override the default output   filter_by_name(     mode='exclude',     dimension='variable',     names='place_holder',     seq_in='names',     seq_fcn=function(x) {      # convert NA count pre group to true/false       x=x>2                  # more the two missing per group       x=rowSums(x)>10        # in more than 10 groups       return(x)     }   ) # apply to transformed data M2 = model_apply(M2,DST)  # get the filtered data DSTF = predicted(M2)"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"missing-value-imputation","dir":"Articles","previous_headings":"Exploratory data analysis of LC-MS-based proteomics and metabolomics datasets (STATegra project) > LC-MS-based proteomics dataset","what":"Missing value imputation","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"STATegra uses two imputation methods available struct objects, create temporary STATegra_impute objects using functions struct package. first imputation method imputes missing values treatment values missing samples using “random value discovery”. create new struct object using set_struct_obj global environment, “method_apply” method implements imputation. second imputation method replacing missing values condition exactly 1 missing value mean values condition. create new struct object corresponding method new object implement filter. new STATegra imputation objects can now used model sequences like struct object. final filter added remove feature missing values imputation.","code":"# create new imputation object set_struct_obj(   class_name = 'STATegra_impute1',   struct_obj = 'model',   params=c(factor_sd='character',factor_name='character'),   outputs=c(imputed='DatasetExperiment'),   prototype = list(     name = 'STATegra imputation 1',     description = 'If missing values are present for all one group then they are replaced with min/2 + \"random value below discovery\".',     predicted = 'imputed'   )  )  # create method_apply for imputation method 1 set_obj_method(   class_name='STATegra_impute1',   method_name='model_apply',   definition=function(M,D) {          # for each feature count NA within each level     na = apply(D$data,2,function(x){       tapply(x,D$sample_meta[[M$factor_name]],function(y){         sum(is.na(y))       })     })     # count number of samples in each group     count=summary(D$sample_meta[[M$factor_name]])     # standard deviation of features within levels of factor_sd     sd = apply(D$data,2,function(x) {tapply(x,D$sample_meta[[M$factor_sd]],sd,na.rm=TRUE)})     sd = median(sd,na.rm=TRUE)          # impute or not     check=na == matrix(count,nrow=2,ncol=ncol(D)) # all missing in one class          # impute matrix     mi = D$data     for (j in 1:nrow(mi)) {       # index of group for this sample       g = which(levels(D$sample_meta[[M$factor_name]])==D$sample_meta[[M$factor_name]][j])       iv=rnorm(ncol(D),min(D$data[j,],na.rm=TRUE)/2,sd)       mi[j,is.na(mi[j,]) & check[g,]] = iv[is.na(mi[j,]) & check[g,]]     }     D$data = mi     M$imputed=D     return(M)   } ) # create new imputation object set_struct_obj(   class_name = 'STATegra_impute2',   struct_obj = 'model',   params=c(factor_name='character'),   outputs=c(imputed='DatasetExperiment'),   prototype = list(     name = 'STATegra imputation 2',     description = 'For those conditions with only 1 NA impute with the mean of the condition.',     predicted = 'imputed'   )  )  # create method_apply for imputation method 2 set_obj_method(   class_name='STATegra_impute2',   method_name='model_apply',   definition=function(M,D) {     # levels in condition     L = levels(D$sample_meta[[M$factor_name]])          # for each feature count NA within each level     na = apply(D$data,2,function(x){       tapply(x,D$sample_meta[[M$factor_name]],function(y){         sum(is.na(y))       })     })          # standard deviation of features within levels of factor_sd     sd = apply(D$data,2,function(x) {tapply(x,D$sample_meta[[M$factor_name]],sd,na.rm=TRUE)})     sd = median(sd,na.rm=TRUE)          # impute or not     check=na == 1 # only one missing for a condition          # index of samples for each condition     IDX = list()     for (k in L) {       IDX[[k]]=which(D$sample_meta[[M$factor_name]]==k)     }          ## impute     # for each feature     for (k in 1:ncol(D)) {       # for each condition       for (j in 1:length(L)) {         # if passes test         if (check[j,k]) {           # mean of samples in group           m = mean(D$data[IDX[[j]],k],na.rm=TRUE)           # imputed value           im = rnorm(1,m,sd)           # replace NA with imputed           D$data[is.na(D$data[,k]) & D$sample_meta[[M$factor_name]]==L[j],k]=im         }       }     }     M$imputed=D     return(M)   } ) # model sequence M3 = STATegra_impute1(factor_name='treatment',factor_sd='condition') +      STATegra_impute2(factor_name = 'condition') +       filter_na_count(threshold = 3, factor_name='condition') # apply model M3 = model_apply(M3,DSTF) # get imputed data DSTFI = predicted(M3) DSTFI ## A \"DatasetExperiment\" object ## ---------------------------- ## name:          STATegra Proteomics ## description:   downloaded from: https://github.com/STATegraData/STATegraData/ ## data:          36 rows x 864 columns ## sample_meta:   36 rows x 4 columns ## variable_meta: 864 rows x 3 columns"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"exploratory-analysis-1","dir":"Articles","previous_headings":"Exploratory data analysis of LC-MS-based proteomics and metabolomics datasets (STATegra project) > LC-MS-based proteomics dataset","what":"Exploratory analysis","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"often useful visualise distribution values across samples verify transformations/normalisation/filtering etc effective.  values longer skewed show approximately normal distribution. boxplots comparable width outliers indicated, transformations etc overall positive effect. PCA used provide graphical representation data. comparison outputs STATegra filter included reduce data include treated samples (IKA)  appear strong batch effect. PC1 dominated time point “24” potentially outlying points time points “2” “0”.","code":"# model sequence P = filter_smeta(mode='include',factor_name='treatment',levels='IKA') +     mean_centre() +      PCA(number_components = 2) # apply model P = model_apply(P,DSTFI)  # scores plots coloured by factors g = list() for (k in c('batch','time')) {   C = pca_scores_plot(factor_name=k,ellipse='none')   g[[k]]=chart_plot(C,P[3]) }  plot_grid(plotlist = g,nrow=1)"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"lc-ms-based-metabolomics-dataset","dir":"Articles","previous_headings":"Exploratory data analysis of LC-MS-based proteomics and metabolomics datasets (STATegra project)","what":"LC-MS-based metabolomics dataset","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"LC-MS-based metabolomics dataset STATegra multi-omics dataset (see Introduction) can found github must extracted zip file prior data analysis. imported data needs converted DatasetExperiment format use structToolbox.","code":"# path to zip zipfile = \"https://raw.github.com/STATegraData/STATegraData/master/Script_STATegra_Metabolomics.zip\"  ## retrieve from BiocFileCache path = bfcrpath(bfc,zipfile) temp = bfccache(bfc)  ## ... or download to temp location # path = tempfile() # temp = tempdir() # download.file(zipfile,path)  # unzip unzip(zipfile=path, files = \"LC_MS_raw_data.xlsx\", exdir=temp)  # read samples data <- as.data.frame(read.xlsx(file.path(temp,\"LC_MS_raw_data.xlsx\"),sheet = 'Data')) # extract sample meta data SM = data[ ,1:8]  # add coloumn for sample type (QC, blank etc) blanks=c(1,2,33,34,65,66) QCs=c(3,4,11,18,25,32,35,36,43,50,57,64) SM$sample_type='Sample' SM$sample_type[blanks]='Blank' SM$sample_type[QCs]='QC'  # put qc/blank labels in all factors for plotting later SM$biol.batch[SM$sample_type!='Sample']=SM$sample_type[SM$sample_type!='Sample'] SM$time.point[SM$sample_type!='Sample']=SM$sample_type[SM$sample_type!='Sample'] SM$condition[SM$sample_type!='Sample']=SM$sample_type[SM$sample_type!='Sample']  # convert to factors SM$biol.batch=ordered(SM$biol.batch,c('9','10','11','12','QC','Blank')) SM$time.point=ordered(SM$time.point,c('0h','2h','6h','12h','18h','24h','QC','Blank')) SM$condition=factor(SM$condition) SM$sample_type=factor(SM$sample_type)  # variable meta data VM = data.frame('annotation'=colnames(data)[9:ncol(data)])  # raw data X = data[,9:ncol(data)] # convert 0 to NA X[X==0]=NA # force to numeric; any non-numerics will become NA X=data.frame(lapply(X,as.numeric),check.names = FALSE)  # make sure row/col names match rownames(X)=data$label rownames(SM)=data$label rownames(VM)=colnames(X)   # create DatasetExperiment object DE = DatasetExperiment(   data = X,    sample_meta = SM,    variable_meta = VM,    name = 'STATegra Metabolomics LCMS',   description = 'https://www.nature.com/articles/s41597-019-0202-7' )  DE ## A \"DatasetExperiment\" object ## ---------------------------- ## name:          STATegra Metabolomics LCMS ## description:   https://www.nature.com/articles/s41597-019-0202-7 ## data:          66 rows x 152 columns ## sample_meta:   66 rows x 9 columns ## variable_meta: 152 rows x 1 columns"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"data-preprocessing-1","dir":"Articles","previous_headings":"Exploratory data analysis of LC-MS-based proteomics and metabolomics datasets (STATegra project) > LC-MS-based metabolomics dataset","what":"Data preprocessing","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"STATegra project LCMS data combined GCMS data multiblock analysis conducted. LCMS explored, data processed differently comparison Gomez-Cabrero et al. basic processing steps applied order generate valid PCA plot biological QC samples.","code":"# prepare model sequence MS = filter_smeta(mode = 'include', levels='QC', factor_name = 'sample_type') +      knn_impute(neighbours=5) +      vec_norm() +       log_transform(base = 10)   # apply model sequence MS = model_apply(MS, DE) ## Warning in knnimp(x, k, maxmiss = rowmax, maxp = maxp): 3 rows with more than 50 % entries missing; ##  mean imputation used for these rows"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"exploratory-analysis-2","dir":"Articles","previous_headings":"Exploratory data analysis of LC-MS-based proteomics and metabolomics datasets (STATegra project) > LC-MS-based metabolomics dataset","what":"Exploratory analysis","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"First use PCA look QC samples order make assessment data quality.  QC labelled “36” clearly different QCs. STATegra QC removed, exclude well. corresponds QC H1. STATegra also excluded QC samples measured immediately blank, also .  Now plot QC samples context samples. several possible approaches, apply approach applying PCA full dataset including QCs. exclude blanks likely dominate plot removed. samples batch 12 excluded STATegra replicate . QCs appear representative samples, strong clusters data, including QC samples biological variation. likely number ‘low quality’ features excluded, now, use sophisticated normalisation (PQN) scaling methods (glog).  can see now QCs tightly clustered. indicates biological variance remaining high quality features much greater technical variance represented QCs. appear trend measurement order (), important indicator instrument drift throughout run large source variation dataset. appear strong clustering related biological batch (B). appear strong trend time (C) likely subtle variation might masked sources variance stage. clustering related condition (D) overlap. explore trends time, split data condition factor explore Ikaros group. Removing condition factor variation potentially make easier spot subtle trends. extract glog transformed matrix previous model sequence continue .  Colouring groups () makes time point trend difficult see, adding ggplot continuous colour scale “viridis” (B) trend time along PC1 becomes much clearer.","code":"# pca model sequence M =  mean_centre() +      PCA(number_components = 3)  # apply model M = model_apply(M,predicted(MS))   # PCA scores plot C = pca_scores_plot(factor_name = 'sample_type',label_factor = 'order',points_to_label = 'all') # plot chart_plot(C,M[2]) # prepare model sequence MS = filter_smeta(       mode = 'include',        levels='QC',        factor_name = 'sample_type') +         filter_by_name(       mode = 'exclude',        dimension='sample',       names = c('1358BZU_0001QC_H1','1358BZU_0001QC_A1','1358BZU_0001QC_G1')) +         knn_impute(       neighbours=5) +         vec_norm() +          log_transform(        base = 10) +          mean_centre() +         PCA(        number_components = 3)  # apply model sequence MS = model_apply(MS, DE) ## Warning in knnimp(x, k, maxmiss = rowmax, maxp = maxp): 4 rows with more than 50 % entries missing; ##  mean imputation used for these rows # PCA scores plot C = pca_scores_plot(factor_name = 'sample_type',label_factor = 'order',points_to_label = 'all') # plot chart_plot(C,MS[7]) # prepare model sequence MS = filter_smeta(       mode = 'exclude',        levels='Blank',        factor_name = 'sample_type') +         filter_smeta(       mode = 'exclude',        levels='12',        factor_name = 'biol.batch') +         filter_by_name(       mode = 'exclude',        dimension='sample',       names = c('1358BZU_0001QC_H1',                 '1358BZU_0001QC_A1',                 '1358BZU_0001QC_G1')) +         knn_impute(       neighbours=5) +         vec_norm() +          log_transform(        base = 10) +          mean_centre() +         PCA(        number_components = 3)  # apply model sequence MS = model_apply(MS, DE) ## Warning in knnimp(x, k, maxmiss = rowmax, maxp = maxp): 2 rows with more than 50 % entries missing; ##  mean imputation used for these rows # PCA scores plots C = pca_scores_plot(factor_name = 'sample_type') # plot chart_plot(C,MS[8]) MS =  filter_smeta(        mode = 'exclude',         levels = '12',         factor_name = 'biol.batch') +          filter_by_name(        mode = 'exclude',         dimension='sample',        names = c('1358BZU_0001QC_H1',                  '1358BZU_0001QC_A1',                  '1358BZU_0001QC_G1')) +        blank_filter(        fold_change = 20,        qc_label = 'QC',        factor_name = 'sample_type') +        filter_smeta(        mode='exclude',        levels='Blank',        factor_name='sample_type') +          mv_feature_filter(        threshold = 80,         qc_label = 'QC',         factor_name = 'sample_type',         method = 'QC') +             mv_feature_filter(         threshold = 50,          factor_name = 'sample_type',          method='across') +         rsd_filter(        rsd_threshold=20,         qc_label='QC',        factor_name='sample_type') +         mv_sample_filter(        mv_threshold = 50) +            pqn_norm(        qc_label='QC',        factor_name='sample_type') +            knn_impute(        neighbours=5,         by='samples') +            glog_transform(        qc_label = 'QC',        factor_name = 'sample_type') +            mean_centre() +             PCA(        number_components = 10)  # apply model sequence MS = model_apply(MS, DE)   # PCA plots using different factors g=list() for (k in c('order','biol.batch','time.point','condition')) {   C = pca_scores_plot(factor_name = k,ellipse='none')   # plot   g[[k]]=chart_plot(C,MS[length(MS)]) }  plot_grid(plotlist = g,align='vh',axis='tblr',nrow=2,labels=c('A','B','C','D')) # get the glog scaled data GL = predicted(MS[11])  # extract the Ikaros group and apply PCA IK = filter_smeta(       mode='include',       factor_name='condition',       levels='Ikaros') +      mean_centre() +       PCA(number_components = 5)  # apply the model sequence to glog transformed data IK = model_apply(IK,GL)  # plot the PCA scores C = pca_scores_plot(factor_name='time.point',ellipse = 'sample') g1=chart_plot(C,IK[3]) g2=g1 + scale_color_viridis_d() # add continuous scale colouring  plot_grid(g1,g2,nrow=2,align='vh',axis = 'tblr',labels=c('A','B'))"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"session-info","dir":"Articles","previous_headings":"","what":"Session Info","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"","code":"sessionInfo() ## R Under development (unstable) (2024-01-23 r85822) ## Platform: x86_64-pc-linux-gnu ## Running under: Ubuntu 22.04.3 LTS ##  ## Matrix products: default ## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3  ## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0 ##  ## locale: ##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               ##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8     ##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8    ##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                  ##  [9] LC_ADDRESS=C               LC_TELEPHONE=C             ## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C        ##  ## time zone: UTC ## tzcode source: system (glibc) ##  ## attached base packages: ## [1] stats     graphics  grDevices utils     datasets  methods   base      ##  ## other attached packages: ##  [1] openxlsx_4.2.5.2     cowplot_1.1.3        gridExtra_2.3        ##  [4] ggplot2_3.4.4        BiocFileCache_2.11.1 dbplyr_2.4.0         ##  [7] ropls_1.35.4         pmp_1.15.0           structToolbox_1.15.1 ## [10] struct_1.15.3        BiocStyle_2.31.0     ##  ## loaded via a namespace (and not attached): ##   [1] DBI_1.2.1                   bitops_1.0-7                ##   [3] MultiDataSet_1.31.0         rlang_1.1.3                 ##   [5] magrittr_2.0.3              e1071_1.7-14                ##   [7] matrixStats_1.2.0           compiler_4.4.0              ##   [9] RSQLite_2.3.5               systemfonts_1.0.5           ##  [11] vctrs_0.6.5                 reshape2_1.4.4              ##  [13] stringr_1.5.1               pkgconfig_2.0.3             ##  [15] crayon_1.5.2                fastmap_1.1.1               ##  [17] XVector_0.43.1              labeling_0.4.3              ##  [19] utf8_1.2.4                  rmarkdown_2.25              ##  [21] itertools_0.1-3             ragg_1.2.7                  ##  [23] bit_4.0.5                   purrr_1.0.2                 ##  [25] xfun_0.41                   MultiAssayExperiment_1.29.0 ##  [27] randomForest_4.7-1.1        zlibbioc_1.49.0             ##  [29] cachem_1.0.8                GenomeInfoDb_1.39.5         ##  [31] jsonlite_1.8.8              progress_1.2.3              ##  [33] blob_1.2.4                  highr_0.10                  ##  [35] DelayedArray_0.29.1         prettyunits_1.2.0           ##  [37] parallel_4.4.0              rols_2.31.0                 ##  [39] R6_2.5.1                    bslib_0.6.1                 ##  [41] stringi_1.8.3               limma_3.59.1                ##  [43] GenomicRanges_1.55.2        jquerylib_0.1.4             ##  [45] Rcpp_1.0.12                 bookdown_0.37               ##  [47] SummarizedExperiment_1.33.3 iterators_1.0.14            ##  [49] knitr_1.45                  IRanges_2.37.1              ##  [51] Matrix_1.6-5                tidyselect_1.2.0            ##  [53] abind_1.4-5                 yaml_2.3.8                  ##  [55] codetools_0.2-19            curl_5.2.0                  ##  [57] doRNG_1.8.6                 lattice_0.22-5              ##  [59] tibble_3.2.1                plyr_1.8.9                  ##  [61] withr_3.0.0                 Biobase_2.63.0              ##  [63] evaluate_0.23               ontologyIndex_2.11          ##  [65] desc_1.4.3                  isoband_0.2.7               ##  [67] proxy_0.4-27                zip_2.3.1                   ##  [69] filelock_1.0.3              pillar_1.9.0                ##  [71] BiocManager_1.30.22         MatrixGenerics_1.15.0       ##  [73] rngtools_1.5.2              foreach_1.5.2               ##  [75] stats4_4.4.0                generics_0.1.3              ##  [77] sp_2.1-3                    RCurl_1.98-1.14             ##  [79] hms_1.1.3                   S4Vectors_0.41.3            ##  [81] munsell_0.5.0               scales_1.3.0                ##  [83] calibrate_1.7.7             class_7.3-22                ##  [85] glue_1.7.0                  tools_4.4.0                 ##  [87] fs_1.6.3                    grid_4.4.0                  ##  [89] impute_1.77.0               missForest_1.5              ##  [91] colorspace_2.1-0            GenomeInfoDbData_1.2.11     ##  [93] cli_3.6.2                   textshaping_0.3.7           ##  [95] fansi_1.0.6                 viridisLite_0.4.2           ##  [97] ggthemes_5.0.0              S4Arrays_1.3.3              ##  [99] dplyr_1.1.4                 pls_2.8-3                   ## [101] pcaMethods_1.95.0           gtable_0.3.4                ## [103] sass_0.4.8                  digest_0.6.34               ## [105] BiocGenerics_0.49.1         SparseArray_1.3.3           ## [107] farver_2.1.1                memoise_2.0.1               ## [109] htmltools_0.5.7             pkgdown_2.0.7.9000          ## [111] lifecycle_1.0.4             httr_1.4.7                  ## [113] statmod_1.5.0               bit64_4.0.5                 ## [115] qqman_0.1.9                 MASS_7.3-60.2"},{"path":"https://computational-metabolomics.github.io/structToolbox/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Gavin Rhys Lloyd. Author, maintainer. Ralf Johannes Maria Weber. Author.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Lloyd, G., Jankevics, ., Weber, R. 2020. struct: R/Bioconductor-based framework standardized metabolomics data analysis beyond. Bioinformatics, 36(22-23), p.5551-5552.","code":"@Article{,   title = {struct: an R/Bioconductor-based framework for standardized metabolomics data analysis and beyond},   author = {Gavin Rhys Lloyd and Andris Jankevics and Ralf Johannes Maria Weber},   journal = {Bioinformatics},   year = {2020},   volume = {36},   number = {22-23},   pages = {5551-5552},   url = {https://doi.org/10.1093/bioinformatics/btaa1031}, }"},{"path":"https://computational-metabolomics.github.io/structToolbox/index.html","id":"structtoolbox","dir":"","previous_headings":"","what":"Data processing & analysis tools for Metabolomics and other omics","title":"Data processing & analysis tools for Metabolomics and other omics","text":"extensive set data (pre-)processing analysis methods tools metabolomics omics, strong emphasis statistics machine learning. toolbox allows user build extensive standardised workflows data analysis. methods tools implemented using class-based templates provided struct (Statistics R Using Class-based Templates) package. toolbox includes pre-processing methods (e.g. signal drift batch correction, normalisation, missing value imputation scaling), univariate (e.g. ttest, various forms ANOVA, Kruskal–Wallis test ) multivariate statistical methods (e.g. PCA PLS, including cross-validation permutation testing) well machine learning methods (e.g. Support Vector Machines). STATistics Ontology (STATO) integrated implemented provide standardised definitions different methods, inputs outputs.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Data processing & analysis tools for Metabolomics and other omics","text":"install package: install development version:","code":"if (!require(\"BiocManager\", quietly = TRUE))     install.packages(\"BiocManager\")  BiocManager::install(\"structToolbox\") if (!require(\"remotes\", quietly = TRUE))     install.packages(\"remotes\")  remotes::install_github(\"computational-metabolomics/structToolbox\")"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ANOVA.html","id":null,"dir":"Reference","previous_headings":"","what":"Analysis of Variance — ANOVA","title":"Analysis of Variance — ANOVA","text":"Analysis Variance (ANOVA) univariate method used analyse difference among group means. Multiple test corrected p-values computed indicate significance feature.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ANOVA.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Analysis of Variance — ANOVA","text":"","code":"ANOVA(alpha = 0.05, mtc = \"fdr\", formula, ss_type = \"III\", ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ANOVA.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Analysis of Variance — ANOVA","text":"alpha (numeric) p-value cutoff determining significance. default 0.05. mtc (character) Multiple test correction method. Allowed values limited following: \"bonferroni\": Bonferroni correction p-values multiplied number comparisons. \"fdr\": Benjamini Hochberg False Discovery Rate correction. \"none\": correction. default \"fdr\". formula (formula) symbolic description model fitted. ss_type (character) ANOVA sum squares. Allowed values limited following: \"\": Type sum squares. \"II\": Type II sum squares. \"III\": Type III sum squares. default \"III\". ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ANOVA.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Analysis of Variance — ANOVA","text":" ANOVA object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ANOVA.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Analysis of Variance — ANOVA","text":"object makes use functionality following packages: car","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ANOVA.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Analysis of Variance — ANOVA","text":"ANOVA object inherits following struct classes: [ANOVA] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ANOVA.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Analysis of Variance — ANOVA","text":"Fox J, Weisberg S (2019). R Companion Applied Regression, Third edition. Sage, Thousand Oaks CA. https://socialsciences.mcmaster.ca/jfox/Books/Companion/.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ANOVA.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Analysis of Variance — ANOVA","text":"","code":"M = ANOVA(       alpha = 0.05,       mtc = \"fdr\",       formula = y ~ x,       ss_type = \"III\")  D = iris_DatasetExperiment() M = ANOVA(formula=y~Species) M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/AUC.html","id":null,"dir":"Reference","previous_headings":"","what":"Area under ROC curve — AUC","title":"Area under ROC curve — AUC","text":"area ROC curve classifier estimated using trapezoid method.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/AUC.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Area under ROC curve — AUC","text":"","code":"AUC(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/AUC.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Area under ROC curve — AUC","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/AUC.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Area under ROC curve — AUC","text":"  AUC  object. object output slots.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/AUC.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Area under ROC curve — AUC","text":"AUC object inherits following struct classes: [AUC] >> [metric] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/AUC.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Area under ROC curve — AUC","text":"","code":"M = AUC()  D = iris_DatasetExperiment() XCV = kfold_xval(folds=5,factor_name='Species') *       (mean_centre() + PLSDA(number_components=2,factor_name='Species')) MET = AUC() XCV = run(XCV,D,MET) #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DFA.html","id":null,"dir":"Reference","previous_headings":"","what":"Discriminant Factor Analysis — DFA","title":"Discriminant Factor Analysis — DFA","text":"Discriminant Factor Analysis (DFA) supervised classification method. Using linear combination input variables, DFA finds new orthogonal axes (canonical values) minimize variance within given class maximize variance classes.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DFA.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Discriminant Factor Analysis — DFA","text":"","code":"DFA(factor_name, number_components = 2, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DFA.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Discriminant Factor Analysis — DFA","text":"factor_name (character) name sample-meta column use. number_components (numeric, integer) number DFA components calculated. default 2. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DFA.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Discriminant Factor Analysis — DFA","text":" DFA object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DFA.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Discriminant Factor Analysis — DFA","text":"DFA object inherits following struct classes: [DFA] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DFA.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Discriminant Factor Analysis — DFA","text":"Manly B (1986). Multivariate Statistical Methods: Primer. Chapman Hall, Boca Raton.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DFA.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Discriminant Factor Analysis — DFA","text":"","code":"M = DFA(       factor_name = \"V1\",       number_components = 2)  D = iris_DatasetExperiment() M = DFA(factor_name='Species') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_boxplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Feature distribution histogram — DatasetExperiment_boxplot","title":"Feature distribution histogram — DatasetExperiment_boxplot","text":"boxplot visualise distribution values within subset features.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_boxplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Feature distribution histogram — DatasetExperiment_boxplot","text":"","code":"DatasetExperiment_boxplot(   factor_name,   by_sample = TRUE,   per_class = TRUE,   number = 50,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_boxplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Feature distribution histogram — DatasetExperiment_boxplot","text":"factor_name (character) name sample-meta column use. by_sample (logical) Plot sample. Allowed values limited following: \"TRUE\": data plotted across features subset samples. \"FALSE\": data plotted across samples subset features. default TRUE. per_class (logical) Plot per class. Allowed values limited following: \"TRUE\": data plotted class. \"FALSE\": data plotted samples. default TRUE. number (numeric, integer) number features/samples plotted. default 50. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_boxplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Feature distribution histogram — DatasetExperiment_boxplot","text":"  DatasetExperiment_boxplot  object. object output slots. See chart_plot struct package plot chart object. struct object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_boxplot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Feature distribution histogram — DatasetExperiment_boxplot","text":"DatasetExperiment_boxplot object inherits following struct classes: [DatasetExperiment_boxplot] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_boxplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Feature distribution histogram — DatasetExperiment_boxplot","text":"","code":"M = DatasetExperiment_boxplot(       factor_name = \"V1\",       by_sample = FALSE,       per_class = FALSE,       number = 50)  D = MTBLS79_DatasetExperiment() C = DatasetExperiment_boxplot(factor_name='Class',number=10,per_class=FALSE) chart_plot(C,D) #> Warning: Removed 585 rows containing non-finite values (`stat_boxplot()`)."},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_dist.html","id":null,"dir":"Reference","previous_headings":"","what":"Feature distribution histogram — DatasetExperiment_dist","title":"Feature distribution histogram — DatasetExperiment_dist","text":"histogram visualise distribution values within features.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_dist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Feature distribution histogram — DatasetExperiment_dist","text":"","code":"DatasetExperiment_dist(factor_name, per_class = TRUE, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_dist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Feature distribution histogram — DatasetExperiment_dist","text":"factor_name (character) name sample-meta column use. per_class (logical) Plot per class. Allowed values limited following: \"TRUE\": distributions plotted class. \"FALSE\": distribution plotted samples. default TRUE. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_dist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Feature distribution histogram — DatasetExperiment_dist","text":"  DatasetExperiment_dist  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_dist.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Feature distribution histogram — DatasetExperiment_dist","text":"DatasetExperiment_dist object inherits following struct classes: [DatasetExperiment_dist] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_dist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Feature distribution histogram — DatasetExperiment_dist","text":"","code":"M = DatasetExperiment_dist(       factor_name = \"V1\",       per_class = FALSE)  D = MTBLS79_DatasetExperiment() C = DatasetExperiment_dist(factor_name='Class') chart_plot(C,D) #> Warning: Removed 9222 rows containing non-finite values (`stat_bin()`)."},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_factor_boxplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Factor boxplot — DatasetExperiment_factor_boxplot","title":"Factor boxplot — DatasetExperiment_factor_boxplot","text":"Boxplot feature visualise distribution values within group","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_factor_boxplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Factor boxplot — DatasetExperiment_factor_boxplot","text":"","code":"DatasetExperiment_factor_boxplot(feature_to_plot, factor_names, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_factor_boxplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Factor boxplot — DatasetExperiment_factor_boxplot","text":"feature_to_plot (character, numeric, integer) name plotted feature. factor_names (character) name sample meta column(s) use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_factor_boxplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Factor boxplot — DatasetExperiment_factor_boxplot","text":"  DatasetExperiment_factor_boxplot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_factor_boxplot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Factor boxplot — DatasetExperiment_factor_boxplot","text":"DatasetExperiment_factor_boxplot object inherits following struct classes: [DatasetExperiment_factor_boxplot] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_factor_boxplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Factor boxplot — DatasetExperiment_factor_boxplot","text":"","code":"M = DatasetExperiment_factor_boxplot(       factor_names = \"V1\",       feature_to_plot = \"V1\")  D = iris_DatasetExperiment() C = DatasetExperiment_factor_boxplot(factor_names='Species',feature_to_plot='Petal.Width') chart_plot(C,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_heatmap.html","id":null,"dir":"Reference","previous_headings":"","what":"DatasetExperiment heatmap — DatasetExperiment_heatmap","title":"DatasetExperiment heatmap — DatasetExperiment_heatmap","text":"heatmap visualise measured values data matrix.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_heatmap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"DatasetExperiment heatmap — DatasetExperiment_heatmap","text":"","code":"DatasetExperiment_heatmap(na_colour = \"#FF00E4\", ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_heatmap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"DatasetExperiment heatmap — DatasetExperiment_heatmap","text":"na_colour (character) hex colour code used plot missing values. default \"#FF00E4\". ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_heatmap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"DatasetExperiment heatmap — DatasetExperiment_heatmap","text":"  DatasetExperiment_heatmap  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_heatmap.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"DatasetExperiment heatmap — DatasetExperiment_heatmap","text":"object makes use functionality following packages: reshape2","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_heatmap.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"DatasetExperiment heatmap — DatasetExperiment_heatmap","text":"DatasetExperiment_heatmap object inherits following struct classes: [DatasetExperiment_heatmap] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_heatmap.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"DatasetExperiment heatmap — DatasetExperiment_heatmap","text":"Wickham H (2007). \"Reshaping Data reshape Package.\" Journal Statistical Software, 21(12), 1-20. http://www.jstatsoft.org/v21/i12/.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_heatmap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"DatasetExperiment heatmap — DatasetExperiment_heatmap","text":"","code":"M = DatasetExperiment_heatmap(       na_colour = \"#FF00E4\")  D = iris_DatasetExperiment() C = DatasetExperiment_heatmap() chart_plot(C,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HCA.html","id":null,"dir":"Reference","previous_headings":"","what":"Hierarchical Cluster Analysis — HCA","title":"Hierarchical Cluster Analysis — HCA","text":"Hierarchical Cluster Analysis numerical technique uses agglomerative clustering identify clusters groupings samples.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HCA.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hierarchical Cluster Analysis — HCA","text":"","code":"HCA(   dist_method = \"euclidean\",   cluster_method = \"complete\",   minkowski_power = 2,   factor_name,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HCA.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hierarchical Cluster Analysis — HCA","text":"dist_method (character) Distance measure. Allowed values limited following: \"euclidean\": euclidean distance (2 norm). \"maximum\": maximum distance. \"manhattan\": absolute distance (1 norm). \"canberra\": weighted version mahattan distance. \"minkowski\": generalisation manhattan euclidean distance nth norm. default \"euclidean\". cluster_method (character) Agglomeration method. Allowed values limited following: \"ward.D\": Ward clustering. \"ward.D2\": Ward clustering using sqaured distances. \"single\": Single linkage. \"complete\": Complete linkage. \"average\": Average linkage (UPGMA). \"mcquitty\": McQuitty linkage (WPGMA). \"median\": Median linkage (WPGMC). \"centroid\": Centroid linkage (UPGMC). default \"complete\". minkowski_power (numeric)  default 2. factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HCA.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Hierarchical Cluster Analysis — HCA","text":" HCA object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HCA.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Hierarchical Cluster Analysis — HCA","text":"object makes use functionality following packages: stats","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HCA.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Hierarchical Cluster Analysis — HCA","text":"HCA object inherits following struct classes: [HCA] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HCA.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Hierarchical Cluster Analysis — HCA","text":"R Core Team (2023). R: Language Environment Statistical Computing. R Foundation Statistical Computing, Vienna, Austria. https://www.R-project.org/.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HCA.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hierarchical Cluster Analysis — HCA","text":"","code":"M = HCA(       dist_method = \"euclidean\",       cluster_method = \"complete\",       minkowski_power = numeric(0),       factor_name = \"V1\")  D = iris_DatasetExperiment() M = HCA(factor_name='Species') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSD.html","id":null,"dir":"Reference","previous_headings":"","what":"Tukey's Honest Significant Difference — HSD","title":"Tukey's Honest Significant Difference — HSD","text":"Tukey's HSD post hoc test modified t-test applied features pairs levels factor. used determine groups different (). multiple test corrected p-value computed indicate groups significantly different others feature.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSD.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tukey's Honest Significant Difference — HSD","text":"","code":"HSD(alpha = 0.05, mtc = \"fdr\", formula, unbalanced = FALSE, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSD.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tukey's Honest Significant Difference — HSD","text":"alpha (numeric) p-value cutoff determining significance. default 0.05. mtc (character) Multiple test correction method. Allowed values limited following: \"bonferroni\": Bonferroni correction p-values multiplied number comparisons. \"fdr\": Benjamini Hochberg False Discovery Rate correction. \"none\": correction. default \"fdr\". formula (formula) symbolic description model fitted. unbalanced (logical) Unbalanced model. Allowed values limited following: \"TRUE\": correction applied unbalanced designs. \"FALSE\": correction applied unbalanced designs. default FALSE. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSD.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tukey's Honest Significant Difference — HSD","text":" HSD object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSD.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tukey's Honest Significant Difference — HSD","text":"object makes use functionality following packages: agricolae","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSD.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Tukey's Honest Significant Difference — HSD","text":"HSD object inherits following struct classes: [HSD] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSD.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Tukey's Honest Significant Difference — HSD","text":"de Mendiburu F (2023). agricolae: Statistical Procedures Agricultural Research. R package version 1.3-7, https://CRAN.R-project.org/package=agricolae.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSD.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tukey's Honest Significant Difference — HSD","text":"","code":"M = HSD(       alpha = 0.05,       mtc = \"fdr\",       formula = y ~ x,       unbalanced = FALSE)  D = iris_DatasetExperiment() M = HSD(formula=y~Species) M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSDEM.html","id":null,"dir":"Reference","previous_headings":"","what":"Tukey's Honest Significant Difference using estimated marginal means — HSDEM","title":"Tukey's Honest Significant Difference using estimated marginal means — HSDEM","text":"Tukey's HSD post hoc test modified t-test applied features pairs levels factor. used determine groups different (). multiple test corrected p-value computed indicate groups significantly different others feature. mixed effects models estimated marginal means used.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSDEM.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tukey's Honest Significant Difference using estimated marginal means — HSDEM","text":"","code":"HSDEM(alpha = 0.05, mtc = \"fdr\", formula, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSDEM.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tukey's Honest Significant Difference using estimated marginal means — HSDEM","text":"alpha (numeric) p-value cutoff determining significance. default 0.05. mtc (character) Multiple test correction method. Allowed values limited following: \"bonferroni\": Bonferroni correction p-values multiplied number comparisons. \"fdr\": Benjamini Hochberg False Discovery Rate correction. \"none\": correction. default \"fdr\". formula (formula) symbolic description model fitted. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSDEM.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tukey's Honest Significant Difference using estimated marginal means — HSDEM","text":" HSDEM object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSDEM.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tukey's Honest Significant Difference using estimated marginal means — HSDEM","text":"object makes use functionality following packages: emmeans nlme","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSDEM.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Tukey's Honest Significant Difference using estimated marginal means — HSDEM","text":"HSDEM object inherits following struct classes: [HSDEM] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSDEM.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Tukey's Honest Significant Difference using estimated marginal means — HSDEM","text":"Lenth R (2023). emmeans: Estimated Marginal Means, aka Least-Squares Means. R package version 1.9.0, https://CRAN.R-project.org/package=emmeans. Pinheiro J, Bates D, R Core Team (2023). nlme: Linear Nonlinear Mixed Effects Models. R package version 3.1-164, https://CRAN.R-project.org/package=nlme. Pinheiro JC, Bates DM (2000). Mixed-Effects Models S S-PLUS. Springer, New York. doi:10.1007/b98882 https://doi.org/10.1007/b98882.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSDEM.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tukey's Honest Significant Difference using estimated marginal means — HSDEM","text":"","code":"M = HSDEM(       alpha = 0.05,       mtc = \"fdr\",       formula = y ~ x)  D = iris_DatasetExperiment() D$sample_meta$id=rownames(D) # dummy id column M = HSDEM(formula = y~Species+ Error(id/Species)) M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/MTBLS79_DatasetExperiment.html","id":null,"dir":"Reference","previous_headings":"","what":"MTBLS79: Direct infusion mass spectrometry metabolomics dataset: a benchmark for data processing and quality control — MTBLS79_DatasetExperiment","title":"MTBLS79: Direct infusion mass spectrometry metabolomics dataset: a benchmark for data processing and quality control — MTBLS79_DatasetExperiment","text":"Direct-infusion mass spectrometry (DIMS) metabolomics important approach characterising molecular responses organisms disease, drugs environment. Increasingly large-scale metabolomics studies conducted, necessitating improvements bioanalytical computational workflows maintain data quality. dataset represents systematic evaluation reproducibility multi-batch DIMS metabolomics study cardiac tissue extracts. comprises twenty biological samples (cow vs. sheep) analysed repeatedly, 8 batches across 7 days, together concurrent set quality control (QC) samples. Data presented step workflow available MetaboLights (https://www.ebi.ac.uk/metabolights/MTBLS79)","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/MTBLS79_DatasetExperiment.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MTBLS79: Direct infusion mass spectrometry metabolomics dataset: a benchmark for data processing and quality control — MTBLS79_DatasetExperiment","text":"","code":"MTBLS79_DatasetExperiment(filtered = FALSE)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/MTBLS79_DatasetExperiment.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"MTBLS79: Direct infusion mass spectrometry metabolomics dataset: a benchmark for data processing and quality control — MTBLS79_DatasetExperiment","text":"filtered TRUE load data quality control filters already applied, FALSE load unfiltered data. Default FALSE. raw data available (https://www.ebi.ac.uk/metabolights/MTBLS79) R dataset pmp package, available Bioconductor.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/MTBLS79_DatasetExperiment.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"MTBLS79: Direct infusion mass spectrometry metabolomics dataset: a benchmark for data processing and quality control — MTBLS79_DatasetExperiment","text":"DatasetExperiment object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/MTBLS79_DatasetExperiment.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"MTBLS79: Direct infusion mass spectrometry metabolomics dataset: a benchmark for data processing and quality control — MTBLS79_DatasetExperiment","text":"","code":"D = MTBLS79_DatasetExperiment() summary(D) #> [1] \"DatasetExperiment object of length 172 with 7 metadata columns\""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/OPLSDA.html","id":null,"dir":"Reference","previous_headings":"","what":"Orthogonal Partial Least Squares regression — OPLSDA","title":"Orthogonal Partial Least Squares regression — OPLSDA","text":"OPLS splits data matrix two parts. One part contains information orthogonal input vector, non-orthogonal.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/OPLSDA.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Orthogonal Partial Least Squares regression — OPLSDA","text":"","code":"OPLSDA(number_components = 1, factor_name, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/OPLSDA.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Orthogonal Partial Least Squares regression — OPLSDA","text":"number_components (numeric, integer) number orthgonal components. default 1. factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/OPLSDA.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Orthogonal Partial Least Squares regression — OPLSDA","text":" OPLSDA object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/OPLSDA.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Orthogonal Partial Least Squares regression — OPLSDA","text":"OPLSDA object inherits following struct classes: [OPLSDA] >> [OPLSR] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/OPLSDA.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Orthogonal Partial Least Squares regression — OPLSDA","text":"","code":"M = OPLSDA(       number_components = 2,       factor_name = \"V1\")  M = OPLSR('number_components'=2,factor_name='Species')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/OPLSR.html","id":null,"dir":"Reference","previous_headings":"","what":"Orthogonal Partial Least Squares regression — OPLSR","title":"Orthogonal Partial Least Squares regression — OPLSR","text":"OPLS splits data matrix two parts. One part contains information orthogonal input vector, non-orthogonal.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/OPLSR.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Orthogonal Partial Least Squares regression — OPLSR","text":"","code":"OPLSR(number_components = 2, factor_name, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/OPLSR.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Orthogonal Partial Least Squares regression — OPLSR","text":"number_components (numeric, integer) number orthgonal components. default 2. factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/OPLSR.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Orthogonal Partial Least Squares regression — OPLSR","text":" OPLSR object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/OPLSR.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Orthogonal Partial Least Squares regression — OPLSR","text":"OPLSR object inherits following struct classes: [OPLSR] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/OPLSR.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Orthogonal Partial Least Squares regression — OPLSR","text":"","code":"M = OPLSR(       number_components = 2,       factor_name = \"V1\")  M = OPLSR('number_components'=2,factor_name='Species')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PCA.html","id":null,"dir":"Reference","previous_headings":"","what":"Principal Component Analysis (PCA) — PCA","title":"Principal Component Analysis (PCA) — PCA","text":"PCA multivariate data reduction technique. summarises data smaller number Principal Components maximise variance.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PCA.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Principal Component Analysis (PCA) — PCA","text":"","code":"PCA(number_components = 2, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PCA.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Principal Component Analysis (PCA) — PCA","text":"number_components (numeric, integer) number Principal Components calculated. default 2. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PCA.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Principal Component Analysis (PCA) — PCA","text":" PCA object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PCA.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Principal Component Analysis (PCA) — PCA","text":"PCA object inherits following struct classes: [PCA] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PCA.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Principal Component Analysis (PCA) — PCA","text":"","code":"M = PCA(       number_components = 2)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSDA.html","id":null,"dir":"Reference","previous_headings":"","what":"Partial least squares discriminant analysis — PLSDA","title":"Partial least squares discriminant analysis — PLSDA","text":"PLS multivariate regression technique extracts latent variables maximising covariance input data response. Discriminant Analysis variant uses group labels response variable. >2 groups 1-vs-approach used. Group membership can predicted test samples based probability estimate group membership, estimated y-value.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSDA.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Partial least squares discriminant analysis — PLSDA","text":"","code":"PLSDA(number_components = 2, factor_name, pred_method = \"max_prob\", ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSDA.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Partial least squares discriminant analysis — PLSDA","text":"number_components (numeric, integer) number PLS components. default 2. factor_name (character) name sample-meta column use. pred_method (character) Prediction method. Allowed values limited following: \"max_yhat\": predicted group selected based largest value y_hat. \"max_prob\": predicted group selected based largest probability group membership. default \"max_prob\". ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSDA.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Partial least squares discriminant analysis — PLSDA","text":" PLSDA object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSDA.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Partial least squares discriminant analysis — PLSDA","text":"object makes use functionality following packages: pls","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSDA.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Partial least squares discriminant analysis — PLSDA","text":"PLSDA object inherits following struct classes: [PLSDA] >> [PLSR] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSDA.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Partial least squares discriminant analysis — PLSDA","text":"Liland K, Mevik B, Wehrens R (2023). pls: Partial Least Squares Principal Component Regression. R package version 2.8-3, https://CRAN.R-project.org/package=pls. Perez NF, Ferre J, Boque R (2009). \"Calculation reliability classification discriminant partial least-squares binary classification.\" Chemometrics Intelligent Laboratory Systems, 95(2), 122-128. Barker M, Rayens W (2003). \"Partial least squares discrimination.\" Journal Chemometrics, 17(3), 166-173.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSDA.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Partial least squares discriminant analysis — PLSDA","text":"","code":"M = PLSDA(       number_components = 2,       factor_name = \"V1\",       pred_method = \"max_prob\")  M = PLSDA('number_components'=2,factor_name='Species')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSR.html","id":null,"dir":"Reference","previous_headings":"","what":"Partial least squares regression — PLSR","title":"Partial least squares regression — PLSR","text":"PLS multivariate regression technique extracts latent variables maximising covariance input data response. regression response continuous variable.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSR.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Partial least squares regression — PLSR","text":"","code":"PLSR(number_components = 2, factor_name, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSR.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Partial least squares regression — PLSR","text":"number_components (numeric, integer) number PLS components. default 2. factor_name (character) name sample meta column(s) use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSR.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Partial least squares regression — PLSR","text":" PLSR object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSR.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Partial least squares regression — PLSR","text":"object makes use functionality following packages: pls","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSR.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Partial least squares regression — PLSR","text":"PLSR object inherits following struct classes: [PLSR] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSR.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Partial least squares regression — PLSR","text":"Liland K, Mevik B, Wehrens R (2023). pls: Partial Least Squares Principal Component Regression. R package version 2.8-3, https://CRAN.R-project.org/package=pls.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSR.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Partial least squares regression — PLSR","text":"","code":"M = PLSR(       number_components = 2,       factor_name = \"V1\")  M = PLSR(factor_name='run_order')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/SVM.html","id":null,"dir":"Reference","previous_headings":"","what":"Support Vector Machine Classifier — SVM","title":"Support Vector Machine Classifier — SVM","text":"Support Vector Machines (SVM) machine learning algorithm classification. can make use kernel functions generate highly non-linear boundaries groups.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/SVM.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Support Vector Machine Classifier — SVM","text":"","code":"SVM(   factor_name,   kernel = \"linear\",   degree = 3,   gamma = 1,   coef0 = 0,   cost = 1,   class_weights = NULL,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/SVM.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Support Vector Machine Classifier — SVM","text":"factor_name (character) name sample-meta column use. kernel (character) Kernel type. Allowed values limited following: \"linear\": . \"polynomial\": . \"radial\": . \"sigmoid\": . default \"linear\". degree (numeric) polynomial degree. default 3. gamma (numeric) gamma parameter. default 1. coef0 (numeric) offset coefficient. default 0. cost (numeric) cost violating constraints. default 1. class_weights (numeric, character, NULL) named vector weights different classes.  Specifying \"inverse\" choose weights inversely proportional class distribution. default NULL. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/SVM.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Support Vector Machine Classifier — SVM","text":" SVM object following output slots: struct object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/SVM.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Support Vector Machine Classifier — SVM","text":"object makes use functionality following packages: e1071","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/SVM.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Support Vector Machine Classifier — SVM","text":"SVM object inherits following struct classes: [SVM] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/SVM.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Support Vector Machine Classifier — SVM","text":"Meyer D, Dimitriadou E, Hornik K, Weingessel , Leisch F (2023). e1071: Misc Functions Department Statistics, Probability Theory Group (Formerly: E1071), TU Wien. R package version 1.7-14, https://CRAN.R-project.org/package=e1071. Brereton RG, Lloyd GR (2010). \"Support Vector Machines classification regression.\" Analyst, 135(2), 230-267.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/SVM.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Support Vector Machine Classifier — SVM","text":"","code":"M = SVM(       factor_name = \"V1\",       kernel = \"linear\",       degree = 3,       gamma = 1,       coef0 = 0,       cost = 1,       class_weights = 1)  M = SVM(factor_name='Species',gamma=1)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/as_data_frame.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert to data.frame — as_data_frame","title":"Convert to data.frame — as_data_frame","text":"Convert outputs input model data.frame.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/as_data_frame.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert to data.frame — as_data_frame","text":"","code":"# S4 method for filter_na_count as_data_frame(M)  # S4 method for ttest as_data_frame(M)  # S4 method for wilcox_test as_data_frame(M)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/as_data_frame.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert to data.frame — as_data_frame","text":"M model object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/as_data_frame.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert to data.frame — as_data_frame","text":"data.frame model outputs","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/as_data_frame.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert to data.frame — as_data_frame","text":"","code":"D = iris_DatasetExperiment() M = filter_na_count(threshold=50,factor_name='Species') M= model_apply(M,D) df = as_data_frame(M)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/autoscale.html","id":null,"dir":"Reference","previous_headings":"","what":"Autoscaling — autoscale","title":"Autoscaling — autoscale","text":"variable/feature mean centred scaled standard deviation. transformed variables zero-mean unit-variance.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/autoscale.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Autoscaling — autoscale","text":"","code":"autoscale(mode = \"data\", ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/autoscale.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Autoscaling — autoscale","text":"mode (character) Mode action. Allowed values limited following: \"data\": Autoscaling applied data matrix . \"sample_meta\": Autoscaling applied sample_meta data . \"\": Autoscaling applied data matrix meta data. default \"data\". ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/autoscale.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Autoscaling — autoscale","text":" autoscale object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/autoscale.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Autoscaling — autoscale","text":"autoscale object inherits following struct classes: [autoscale] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/autoscale.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Autoscaling — autoscale","text":"","code":"M = autoscale(       mode = \"data\")  D = iris_DatasetExperiment() M = autoscale() M = model_train(M,D) M = model_predict(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/balanced_accuracy.html","id":null,"dir":"Reference","previous_headings":"","what":"Balanced Accuracy — balanced_accuracy","title":"Balanced Accuracy — balanced_accuracy","text":"Balanced Accuracy average proportion correctly classified samples across groups.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/balanced_accuracy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Balanced Accuracy — balanced_accuracy","text":"","code":"balanced_accuracy(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/balanced_accuracy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Balanced Accuracy — balanced_accuracy","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/balanced_accuracy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Balanced Accuracy — balanced_accuracy","text":"  balanced_accuracy  object. object output slots.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/balanced_accuracy.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Balanced Accuracy — balanced_accuracy","text":"balanced_accuracy object inherits following struct classes: [balanced_accuracy] >> [metric] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/balanced_accuracy.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Balanced Accuracy — balanced_accuracy","text":"","code":"M = balanced_accuracy()  D = iris_DatasetExperiment() XCV = kfold_xval(folds=5,factor_name='Species') *       (mean_centre() + PLSDA(number_components=2,factor_name='Species')) MET = balanced_accuracy() XCV = run(XCV,D,MET)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/blank_filter.html","id":null,"dir":"Reference","previous_headings":"","what":"Blank filter — blank_filter","title":"Blank filter — blank_filter","text":"blank filter filters features comparing median intensity blank samples median intensity samples. Features relative intensity (fold change) large compared blank removed. number times feature detected across blank samples may also considered. feature detected high enough proportion blanks removed.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/blank_filter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Blank filter — blank_filter","text":"","code":"blank_filter(   fold_change = 20,   blank_label = \"blank\",   qc_label = \"QC\",   factor_name,   fraction_in_blank = 0,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/blank_filter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Blank filter — blank_filter","text":"fold_change (numeric) Features fold change less value removed. default 20. blank_label (character) label used identify blank samples. default \"blank\". qc_label (character, NULL) label used identify QC samples. set NULL median samples used. default \"QC\". factor_name (character) name sample-meta column use. fraction_in_blank (numeric) Features present less proportion blanks considered removal. default 0. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/blank_filter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Blank filter — blank_filter","text":" blank_filter object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/blank_filter.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Blank filter — blank_filter","text":"object makes use functionality following packages: pmp","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/blank_filter.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Blank filter — blank_filter","text":"blank_filter object inherits following struct classes: [blank_filter] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/blank_filter.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Blank filter — blank_filter","text":"Jankevics , Lloyd GR, Weber RJM (2023). pmp: Peak Matrix Processing signal batch correction metabolomics datasets. doi:10.18129/B9.bioc.pmp https://doi.org/10.18129/B9.bioc.pmp, R package version 1.14.0, https://bioconductor.org/packages/pmp.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/blank_filter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Blank filter — blank_filter","text":"","code":"M = blank_filter(       fold_change = 20,       blank_label = \"Blank\",       qc_label = \"QC\",       factor_name = \"V1\",       fraction_in_blank = 0)  D = iris_DatasetExperiment() M = blank_filter(fold_change=2,                  factor_name='Species',                  blank_label='setosa',                  qc_label='versicolor') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/blank_filter_hist.html","id":null,"dir":"Reference","previous_headings":"","what":"Histogram of blank filter fold changes — blank_filter_hist","title":"Histogram of blank filter fold changes — blank_filter_hist","text":"histogram calculated fold changes blank filter (median samples divided median blanks)","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/blank_filter_hist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Histogram of blank filter fold changes — blank_filter_hist","text":"","code":"blank_filter_hist(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/blank_filter_hist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Histogram of blank filter fold changes — blank_filter_hist","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/blank_filter_hist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Histogram of blank filter fold changes — blank_filter_hist","text":"  blank_filter_hist  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/blank_filter_hist.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Histogram of blank filter fold changes — blank_filter_hist","text":"blank_filter_hist object inherits following struct classes: [blank_filter_hist] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/blank_filter_hist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Histogram of blank filter fold changes — blank_filter_hist","text":"","code":"M = blank_filter_hist()  C = blank_filter_hist()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/bootstrap.html","id":null,"dir":"Reference","previous_headings":"","what":"Bootstrap resampling — bootstrap","title":"Bootstrap resampling — bootstrap","text":"bootstrap resampling subset samples selected random replacement form training set. sample selected training included test set. process repeated many times, performance metrics computed repetition.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/bootstrap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bootstrap resampling — bootstrap","text":"","code":"bootstrap(number_of_repetitions = 100, collect, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/bootstrap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bootstrap resampling — bootstrap","text":"number_of_repetitions (numeric, integer) number bootstrap repetitions. default 100. collect (character) name model output collect bootstrap repetitions, addition input metric. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/bootstrap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bootstrap resampling — bootstrap","text":" bootstrap object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/bootstrap.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Bootstrap resampling — bootstrap","text":"bootstrap object inherits following struct classes: [bootstrap] >> [resampler] >> [iterator] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/bootstrap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bootstrap resampling — bootstrap","text":"","code":"M = bootstrap(       number_of_repetitions = 10,       collect = \"vip\")  I = bootstrap(number_of_repetitions = 10, collect = 'vip')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/calculate.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate metric — calculate,AUC-method","title":"Calculate metric — calculate,AUC-method","text":"Calculate metric","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/calculate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate metric — calculate,AUC-method","text":"","code":"# S4 method for AUC calculate(obj, Y, Yhat)  # S4 method for balanced_accuracy calculate(obj, Y, Yhat)  # S4 method for r_squared calculate(obj, Y, Yhat)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/calculate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate metric — calculate,AUC-method","text":"obj metric object Y true values/group labels Yhat predicted values/group labels","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/calculate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate metric — calculate,AUC-method","text":"modified metric object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/calculate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate metric — calculate,AUC-method","text":"","code":"MET = metric() calculate(MET) #> Warning: no calculation provided for this metric #> A \"metric\" object #> ----------------- #> name:           #> description:    #> value:          #>"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/chart_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"chart_plot method — chart_plot,dfa_scores_plot,DFA-method","title":"chart_plot method — chart_plot,dfa_scores_plot,DFA-method","text":"Plots chart object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/chart_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"chart_plot method — chart_plot,dfa_scores_plot,DFA-method","text":"","code":"# S4 method for dfa_scores_plot,DFA chart_plot(obj, dobj)  # S4 method for scatter_chart,DatasetExperiment chart_plot(obj, dobj)  # S4 method for pca_correlation_plot,PCA chart_plot(obj, dobj)  # S4 method for pca_scores_plot,PCA chart_plot(obj, dobj)  # S4 method for pca_biplot,PCA chart_plot(obj, dobj)  # S4 method for pca_loadings_plot,PCA chart_plot(obj, dobj)  # S4 method for pca_scree_plot,PCA chart_plot(obj, dobj)  # S4 method for pca_dstat_plot,PCA chart_plot(obj, dobj)  # S4 method for plsr_prediction_plot,PLSR chart_plot(obj, dobj)  # S4 method for plsr_residual_hist,PLSR chart_plot(obj, dobj)  # S4 method for plsr_qq_plot,PLSR chart_plot(obj, dobj)  # S4 method for plsr_cook_dist,PLSR chart_plot(obj, dobj)  # S4 method for pls_scores_plot,PLSR chart_plot(obj, dobj)  # S4 method for plsda_predicted_plot,PLSDA chart_plot(obj, dobj)  # S4 method for plsda_roc_plot,PLSDA chart_plot(obj, dobj)  # S4 method for pls_vip_plot,PLSR chart_plot(obj, dobj)  # S4 method for pls_regcoeff_plot,PLSR chart_plot(obj, dobj)  # S4 method for blank_filter_hist,blank_filter chart_plot(obj, dobj)  # S4 method for confounders_lsq_barchart,confounders_clsq chart_plot(obj, dobj)  # S4 method for confounders_lsq_boxplot,confounders_clsq chart_plot(obj, dobj)  # S4 method for feature_boxplot,DatasetExperiment chart_plot(obj, dobj)  # S4 method for mv_histogram,DatasetExperiment chart_plot(obj, dobj)  # S4 method for mv_boxplot,DatasetExperiment chart_plot(obj, dobj)  # S4 method for DatasetExperiment_dist,DatasetExperiment chart_plot(obj, dobj)  # S4 method for DatasetExperiment_boxplot,DatasetExperiment chart_plot(obj, dobj)  # S4 method for compare_dist,DatasetExperiment chart_plot(obj, dobj, eobj)  # S4 method for DatasetExperiment_heatmap,DatasetExperiment chart_plot(obj, dobj)  # S4 method for DatasetExperiment_factor_boxplot,DatasetExperiment chart_plot(obj, dobj)  # S4 method for feature_profile_array,DatasetExperiment chart_plot(obj, dobj)  # S4 method for feature_profile,DatasetExperiment chart_plot(obj, dobj)  # S4 method for fold_change_plot,fold_change chart_plot(obj, dobj)  # S4 method for fs_line,forward_selection_by_rank chart_plot(obj, dobj)  # S4 method for glog_opt_plot,glog_transform chart_plot(obj, dobj, gobj)  # S4 method for gs_line,grid_search_1d chart_plot(obj, dobj)  # S4 method for hca_dendrogram,HCA chart_plot(obj, dobj)  # S4 method for kfoldxcv_grid,kfold_xval chart_plot(obj, dobj)  # S4 method for kfoldxcv_metric,kfold_xval chart_plot(obj, dobj)  # S4 method for kw_p_hist,kw_rank_sum chart_plot(obj, dobj)  # S4 method for mv_feature_filter_hist,mv_feature_filter chart_plot(obj, dobj)  # S4 method for mv_sample_filter_hist,mv_sample_filter chart_plot(obj, dobj)  # S4 method for permutation_test_plot,permutation_test chart_plot(obj, dobj)  # S4 method for plsda_feature_importance_plot,PLSDA chart_plot(obj, dobj)  # S4 method for pqn_norm_hist,pqn_norm chart_plot(obj, dobj)  # S4 method for resample_chart,resample chart_plot(obj, dobj)  # S4 method for rsd_filter_hist,rsd_filter chart_plot(obj, dobj)  # S4 method for feature_profile,sb_corr chart_plot(obj, dobj, gobj)  # S4 method for svm_plot_2d,SVM chart_plot(obj, dobj, gobj)  # S4 method for tSNE_scatter,tSNE chart_plot(obj, dobj)  # S4 method for tic_chart,DatasetExperiment chart_plot(obj, dobj)  # S4 method for wilcox_p_hist,wilcox_test chart_plot(obj, dobj)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/chart_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"chart_plot method — chart_plot,dfa_scores_plot,DFA-method","text":"obj chart object dobj struct object eobj second DatasetExperiment object compare first gobj DatasetExperiment object signal correction applied.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/chart_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"chart_plot method — chart_plot,dfa_scores_plot,DFA-method","text":"plot object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/chart_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"chart_plot method — chart_plot,dfa_scores_plot,DFA-method","text":"","code":"C = example_chart() chart_plot(C,iris_DatasetExperiment()) #> Warning: no chart defined for \"DatasetExperiment\" #> A \"example_chart\" object #> ------------------------ #> name:           #> description:"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/classical_lsq.html","id":null,"dir":"Reference","previous_headings":"","what":"Univariate Classical Least Squares Regression — classical_lsq","title":"Univariate Classical Least Squares Regression — classical_lsq","text":"univariate classical least squares regression line fitted feature/variable response variable. fitted line minimises sum squared differences true response predicted response. coefficients (offset, gradient) fit can tested significance.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/classical_lsq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Univariate Classical Least Squares Regression — classical_lsq","text":"","code":"classical_lsq(alpha = 0.05, mtc = \"fdr\", factor_names, intercept = TRUE, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/classical_lsq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Univariate Classical Least Squares Regression — classical_lsq","text":"alpha (numeric) p-value cutoff determining significance. default 0.05. mtc (character) Multiple test correction method. Allowed values limited following: \"bonferroni\": Bonferroni correction p-values multiplied number comparisons. \"fdr\": Benjamini Hochberg False Discovery Rate correction. \"none\": correction. default \"fdr\". factor_names (character, list) column names regress . character vector list used ofr features. list character vectors provided assumed different set columns feature. intercept (logical) Model intercept. Allowed values limited following: \"TRUE\": intercept term included model. \"FALSE\": intercept term included model. default TRUE. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/classical_lsq.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Univariate Classical Least Squares Regression — classical_lsq","text":" classical_lsq object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/classical_lsq.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Univariate Classical Least Squares Regression — classical_lsq","text":"classical_lsq object inherits following struct classes: [classical_lsq] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/classical_lsq.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Univariate Classical Least Squares Regression — classical_lsq","text":"","code":"M = classical_lsq(       alpha = 0.05,       mtc = \"fdr\",       factor_names = \"V1\",       intercept = FALSE)  D = iris_DatasetExperiment() M = classical_lsq(factor_names = 'Species') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/compare_dist.html","id":null,"dir":"Reference","previous_headings":"","what":"Compare distributions — compare_dist","title":"Compare distributions — compare_dist","text":"Histograms boxplots computed across samples features used visually compare two datasets e.g. filtering /normalisation.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/compare_dist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compare distributions — compare_dist","text":"","code":"compare_dist(factor_name, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/compare_dist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compare distributions — compare_dist","text":"factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/compare_dist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compare distributions — compare_dist","text":"  compare_dist  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/compare_dist.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Compare distributions — compare_dist","text":"compare_dist object inherits following struct classes: [compare_dist] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/compare_dist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compare distributions — compare_dist","text":"","code":"M = compare_dist(       factor_name = \"V1\")  D1=MTBLS79_DatasetExperiment(filtered=FALSE) D2=MTBLS79_DatasetExperiment(filtered=TRUE) C = compare_dist(factor_name='Class') chart_plot(C,D1,D2) #> Warning: Removed 109 rows containing non-finite values (`stat_boxplot()`). #> Warning: Removed 109 rows containing non-finite values (`stat_boxplot()`). #> Warning: Removed 8012 rows containing non-finite values (`stat_bin()`). #> Warning: Removed 8012 rows containing non-finite values (`stat_bin()`). #> Warning: Removed 10623 rows containing non-finite values (`stat_bin()`). #> Warning: Removed 6 rows containing missing values (`geom_path()`). #> Warning: Removed 10623 rows containing non-finite values (`stat_bin()`). #> Warning: Removed 6 rows containing missing values (`geom_path()`). #> Warning: Removed 109 rows containing non-finite values (`stat_boxplot()`). #> Warning: Removed 109 rows containing non-finite values (`stat_boxplot()`).  #> TableGrob (4 x 2) \"arrange\": 4 grobs #>   z     cells    name           grob #> 1 1 (1-1,1-1) arrange gtable[layout] #> 2 2 (1-1,2-2) arrange gtable[layout] #> 3 3 (2-4,1-1) arrange gtable[layout] #> 4 4 (2-4,2-2) arrange gtable[layout]"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_clsq.html","id":null,"dir":"Reference","previous_headings":"","what":"Check for confounding factors — confounders_clsq","title":"Check for confounding factors — confounders_clsq","text":"Univariate least squares regression models used compare models without potential confounding factors included. change coefficients (delta) computed potential confounding factor. Factors large delta said large impact model therefore confounding. p-values computed models confounders included reduce potential false positives. suitable main factors 2 levels.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_clsq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check for confounding factors — confounders_clsq","text":"","code":"confounders_clsq(   alpha = 0.05,   mtc = \"fdr\",   factor_name,   confounding_factors,   threshold = 0.15,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_clsq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check for confounding factors — confounders_clsq","text":"alpha (numeric) p-value cutoff determining significance. default 0.05. mtc (character) Multiple test correction method. Allowed values limited following: \"bonferroni\": Bonferroni correction p-values multiplied number comparisons. \"fdr\": Benjamini Hochberg False Discovery Rate correction. \"none\": correction. default \"fdr\". factor_name (character) name main factor factors may confounding. confounding_factors (character) name(s) factor(s) potential confounding factors. threshold (numeric) Factors delta greater threshold considered confounding. default 0.15. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_clsq.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check for confounding factors — confounders_clsq","text":" confounders_clsq object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_clsq.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Check for confounding factors — confounders_clsq","text":"confounders_clsq object inherits following struct classes: [confounders_clsq] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_clsq.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check for confounding factors — confounders_clsq","text":"","code":"M = confounders_clsq(       alpha = 0.05,       mtc = \"fdr\",       factor_name = character(0),       confounding_factors = character(0),       threshold = 0.15)  D = MTBLS79_DatasetExperiment() M = filter_by_name(mode='include',dimension='variable',         names=colnames(D$data)[1:10]) + # first 10 features     filter_smeta(mode='exclude',levels='QC',         factor_name='Class') + # reduce to two group comparison     confounders_clsq(factor_name = 'Class',         confounding_factors=c('run_order','Batch')) M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_lsq_barchart.html","id":null,"dir":"Reference","previous_headings":"","what":"Confounding factor relative change barchart — confounders_lsq_barchart","title":"Confounding factor relative change barchart — confounders_lsq_barchart","text":"barchart relative change (delta) regression coefficient potential confounding factors included, excluded, model. Factors large delta considered confounding factors.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_lsq_barchart.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Confounding factor relative change barchart — confounders_lsq_barchart","text":"","code":"confounders_lsq_barchart(feature_to_plot, threshold = 10, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_lsq_barchart.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Confounding factor relative change barchart — confounders_lsq_barchart","text":"feature_to_plot (numeric, character, integer) column name feature plotted. threshold (numeric) horizontal line plotted indicate threshold. default 10. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_lsq_barchart.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Confounding factor relative change barchart — confounders_lsq_barchart","text":"  confounders_lsq_barchart  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_lsq_barchart.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Confounding factor relative change barchart — confounders_lsq_barchart","text":"confounders_lsq_barchart object inherits following struct classes: [confounders_lsq_barchart] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_lsq_barchart.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Confounding factor relative change barchart — confounders_lsq_barchart","text":"","code":"M = confounders_lsq_barchart(       feature_to_plot = 1,       threshold = 10)  D = MTBLS79_DatasetExperiment() M = filter_by_name(mode='include',dimension='variable',         names=colnames(D$data)[1:10]) + # first 10 features     filter_smeta(mode='exclude',levels='QC',         factor_name='Class') + # reduce to two group comparison     confounders_clsq(factor_name = 'Class',         confounding_factors=c('run_order','Batch')) M = model_apply(M,D) C = C=confounders_lsq_barchart(feature_to_plot=1,threshold=15) chart_plot(C,M[3])"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_lsq_boxplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Confounding factor relative change boxplot — confounders_lsq_boxplot","title":"Confounding factor relative change boxplot — confounders_lsq_boxplot","text":"boxplot relative change (delta) regression coefficient potential confounding factors included, excluded, model. Factors large delta considered confounding factors.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_lsq_boxplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Confounding factor relative change boxplot — confounders_lsq_boxplot","text":"","code":"confounders_lsq_boxplot(threshold = 10, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_lsq_boxplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Confounding factor relative change boxplot — confounders_lsq_boxplot","text":"threshold (numeric) horizontal line plotted indicate threshold. default 10. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_lsq_boxplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Confounding factor relative change boxplot — confounders_lsq_boxplot","text":"  confounders_lsq_boxplot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_lsq_boxplot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Confounding factor relative change boxplot — confounders_lsq_boxplot","text":"confounders_lsq_boxplot object inherits following struct classes: [confounders_lsq_boxplot] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_lsq_boxplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Confounding factor relative change boxplot — confounders_lsq_boxplot","text":"","code":"M = confounders_lsq_boxplot(       threshold = 10)  D = MTBLS79_DatasetExperiment() M = filter_by_name(mode='include',dimension='variable',         names=colnames(D$data)[1:10]) + # first 10 features     filter_smeta(mode='exclude',levels='QC',         factor_name='Class') + # reduce to two group comparison     confounders_clsq(factor_name = 'Class',         confounding_factors=c('run_order','Batch')) M = model_apply(M,D) C = C=confounders_lsq_boxplot(threshold=15) chart_plot(C,M[3])"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/constant_sum_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"Normalisation to constant sum — constant_sum_norm","title":"Normalisation to constant sum — constant_sum_norm","text":"sample normalised total signal equal one (scaling factor specified).","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/constant_sum_norm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Normalisation to constant sum — constant_sum_norm","text":"","code":"constant_sum_norm(scaling_factor = 1, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/constant_sum_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Normalisation to constant sum — constant_sum_norm","text":"scaling_factor (numeric) scaling factor applied normalisation. default 1. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/constant_sum_norm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Normalisation to constant sum — constant_sum_norm","text":" constant_sum_norm object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/constant_sum_norm.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Normalisation to constant sum — constant_sum_norm","text":"constant_sum_norm object inherits following struct classes: [constant_sum_norm] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/constant_sum_norm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Normalisation to constant sum — constant_sum_norm","text":"","code":"M = constant_sum_norm(       scaling_factor = 1)  M = constant_sum_norm()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/corr_coef.html","id":null,"dir":"Reference","previous_headings":"","what":"Correlation coefficient — corr_coef","title":"Correlation coefficient — corr_coef","text":"correlation features set continuous factor calculated. Multiple-test corrected p-values used indicate whether computed coefficients may occurred chance.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/corr_coef.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Correlation coefficient — corr_coef","text":"","code":"corr_coef(alpha = 0.05, mtc = \"fdr\", factor_names, method = \"spearman\", ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/corr_coef.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Correlation coefficient — corr_coef","text":"alpha (numeric) p-value cutoff determining significance. default 0.05. mtc (character) Multiple test correction method. Allowed values limited following: \"bonferroni\": Bonferroni correction p-values multiplied number comparisons. \"fdr\": Benjamini Hochberg False Discovery Rate correction. \"none\": correction. default \"fdr\". factor_names (character) name sample meta column(s) use. method (character) Type correlation. Allowed values limited following: \"kendall\": Kendall's tau computed. \"pearson\": Pearson product moment correlation computed. \"spearman\": Spearman's rho statistic computed. default \"spearman\". ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/corr_coef.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Correlation coefficient — corr_coef","text":" corr_coef object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/corr_coef.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Correlation coefficient — corr_coef","text":"object makes use functionality following packages: stats","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/corr_coef.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Correlation coefficient — corr_coef","text":"corr_coef object inherits following struct classes: [corr_coef] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/corr_coef.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Correlation coefficient — corr_coef","text":"R Core Team (2023). R: Language Environment Statistical Computing. R Foundation Statistical Computing, Vienna, Austria. https://www.R-project.org/.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/corr_coef.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Correlation coefficient — corr_coef","text":"","code":"M = corr_coef(       alpha = 0.05,       mtc = \"fdr\",       factor_names = \"V1\",       method = \"spearman\")  D = MTBLS79_DatasetExperiment(filtered=TRUE)  # subset for this example D = D[,1:10]  # convert to numeric for this example D$sample_meta$sample_order=as.numeric(D$sample_meta$run_order) D$sample_meta$sample_rep=as.numeric(D$sample_meta$Sample_Rep)  M = corr_coef(factor_names=c('sample_order','sample_rep')) M = model_apply(M,D) #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dfa_scores_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"DFA scores plot — dfa_scores_plot","title":"DFA scores plot — dfa_scores_plot","text":"scatter plot selected DFA components.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dfa_scores_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"DFA scores plot — dfa_scores_plot","text":"","code":"dfa_scores_plot(   components = c(1, 2),   points_to_label = \"none\",   factor_name,   ellipse = \"all\",   label_filter = character(0),   label_factor = \"rownames\",   label_size = 3.88,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dfa_scores_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"DFA scores plot — dfa_scores_plot","text":"components (numeric) components selected plotting. default c(1, 2). points_to_label (character) Points label. Allowed values limited following: \"none\": samples labels displayed. \"\": labels samples displayed. \"outliers\": Labels potential outlier samples displayed. default \"none\". factor_name (character) name sample-meta column use. ellipse (character) Plot ellipses. Allowed values limited following: \"\": Hotelling T2 ellipses (p=0.95) plotted groups samples. \"group\": Hotelling T2 ellipses (p=0.95) plotted groups. \"none\": Ellipses included plot. \"sample\": Hotelling T2 ellipse (p=0.95) plotted samples (ignoring group). default \"\". label_filter (character) Labels plotted named groups. zero-length groups included. default character(0). label_factor (character) column name sample_meta use labelling samples plot. \"rownames\" use row names sample_meta. default \"rownames\". label_size (numeric) text size labels. Note Font Units. default 3.88. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dfa_scores_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"DFA scores plot — dfa_scores_plot","text":"  dfa_scores_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dfa_scores_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"DFA scores plot — dfa_scores_plot","text":"object makes use functionality following packages: scales ggplot2","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dfa_scores_plot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"DFA scores plot — dfa_scores_plot","text":"dfa_scores_plot object inherits following struct classes: [dfa_scores_plot] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dfa_scores_plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"DFA scores plot — dfa_scores_plot","text":"Wickham H, Pedersen T, Seidel D (2023). scales: Scale Functions Visualization. R package version 1.3.0, https://CRAN.R-project.org/package=scales. Wickham H (2016). ggplot2: Elegant Graphics Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dfa_scores_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"DFA scores plot — dfa_scores_plot","text":"","code":"M = dfa_scores_plot(       components = c(1, 2),       points_to_label = \"none\",       factor_name = \"V1\",       ellipse = \"all\",       label_filter = character(0),       label_factor = \"rownames\",       label_size = 3.88)  D = iris_DatasetExperiment() M = mean_centre() + DFA(factor_name='Species') M = model_apply(M,D) C = dfa_scores_plot(factor_name = 'Species') chart_plot(C,M[2]) #> Warning: The following aesthetics were dropped during statistical transformation: label #> ℹ This can happen when ggplot fails to infer the correct grouping structure in #>   the data. #> ℹ Did you forget to specify a `group` aesthetic or to convert a numerical #>   variable into a factor? #> Warning: The following aesthetics were dropped during statistical transformation: label #> ℹ This can happen when ggplot fails to infer the correct grouping structure in #>   the data. #> ℹ Did you forget to specify a `group` aesthetic or to convert a numerical #>   variable into a factor? #> Warning: The following aesthetics were dropped during statistical transformation: label #> ℹ This can happen when ggplot fails to infer the correct grouping structure in #>   the data. #> ℹ Did you forget to specify a `group` aesthetic or to convert a numerical #>   variable into a factor? #> Warning: The following aesthetics were dropped during statistical transformation: label #> ℹ This can happen when ggplot fails to infer the correct grouping structure in #>   the data. #> ℹ Did you forget to specify a `group` aesthetic or to convert a numerical #>   variable into a factor?"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dratio_filter.html","id":null,"dir":"Reference","previous_headings":"","what":"Dispersion ratio filter — dratio_filter","title":"Dispersion ratio filter — dratio_filter","text":"dispersion ratio (d-ratio) compares standard deviation (non-parametric equivalent) Quality Control (QC) samples relative standard deviation (non-parametric equivalent) samples feature. d-ratio greater predefined threshold observed sample variance due technical variance feature removed.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dratio_filter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dispersion ratio filter — dratio_filter","text":"","code":"dratio_filter(   threshold = 20,   qc_label = \"QC\",   factor_name,   method = \"ratio\",   dispersion = \"sd\",   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dratio_filter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dispersion ratio filter — dratio_filter","text":"threshold (numeric) threshold features removed. default 20. qc_label (character) label used identify QC samples. default \"QC\". factor_name (character) name sample-meta column use. method (character) dratio method. Allowed values limited following: \"ratio\": Dispersion QCs divided dispersion samples. Corresponds Eq 4  Broadhurst et al (2018). \"euclidean\": Dispersion QCs divided euclidean length total dispersion. Total dispersion estimated QC Sample dispersion assuming orthogonal. Corresponds Eq 5 Broadhurst et al (2018). default \"ratio\". dispersion (character) Dispersion method. Allowed values limited following: \"sd\": Dispersion estimated using standard deviation. \"mad\": Dispersion estimated using median absolute deviation. default \"sd\". ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dratio_filter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Dispersion ratio filter — dratio_filter","text":" dratio_filter object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dratio_filter.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Dispersion ratio filter — dratio_filter","text":"dratio_filter object inherits following struct classes: [dratio_filter] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dratio_filter.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Dispersion ratio filter — dratio_filter","text":"Broadhurst D, Goodacre R, Reinke SN, Kuligowski J, Wilson ID, Lewis MR, Dunn WB (2018). \"Guidelines considerations use system suitability quality control samples mass spectrometry assays applied untargeted clinical metabolomic studies.\" Metabolomics, 14(6).","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dratio_filter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Dispersion ratio filter — dratio_filter","text":"","code":"M = dratio_filter(       threshold = 20,       qc_label = \"QC\",       factor_name = \"V1\",       method = \"ratio\",       dispersion = \"sd\")  D = MTBLS79_DatasetExperiment() M = dratio_filter(threshold=20,qc_label='QC',factor_name='Class') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/equal_split.html","id":null,"dir":"Reference","previous_headings":"","what":"Equal group sized sampling — equal_split","title":"Equal group sized sampling — equal_split","text":"Samples randomly chosen level training set equal numbers samples levels. number samples based input proportion smallest group size.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/equal_split.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Equal group sized sampling — equal_split","text":"","code":"equal_split(p_train = 1, factor_name, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/equal_split.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Equal group sized sampling — equal_split","text":"p_train (numeric) proportion samples selected training set. default 1. factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/equal_split.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Equal group sized sampling — equal_split","text":" equal_split object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/equal_split.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Equal group sized sampling — equal_split","text":"equal_split object inherits following struct classes: [equal_split] >> [split_data] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/equal_split.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Equal group sized sampling — equal_split","text":"","code":"M = equal_split(       factor_name = \"V1\",       p_train = 0.75)  D = iris_DatasetExperiment() M = equal_split(factor_name='Species') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_boxplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Feature boxplot — feature_boxplot","title":"Feature boxplot — feature_boxplot","text":"boxplot visualise distribution values within feature.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_boxplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Feature boxplot — feature_boxplot","text":"","code":"feature_boxplot(   label_outliers = TRUE,   feature_to_plot,   factor_name,   show_counts = TRUE,   style = \"boxplot\",   jitter = FALSE,   fill = FALSE,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_boxplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Feature boxplot — feature_boxplot","text":"label_outliers (logical) Label outliers. Allowed values limited following: \"TRUE\": index outlier samples included plot. \"FALSE\": labels displayed. default TRUE. feature_to_plot (character, numeric, integer) column name plotted feature. factor_name (character) name sample-meta column use. show_counts (logical) Show counts. Allowed values limited following: \"TRUE\": number samples box displayed. \"FALSE\": number samples box displayed. default TRUE. style (character) Plot style. Allowed values limited following: \"boxplot\": Boxplot style. \"violin\": Violon plot style. default \"boxplot\". jitter (logical) Include points plotted added jitter. default FALSE. fill (logical) Block fill boxes violins group colour. default FALSE. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_boxplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Feature boxplot — feature_boxplot","text":"  feature_boxplot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_boxplot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Feature boxplot — feature_boxplot","text":"feature_boxplot object inherits following struct classes: [feature_boxplot] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_boxplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Feature boxplot — feature_boxplot","text":"","code":"M = feature_boxplot(       label_outliers = FALSE,       feature_to_plot = \"V1\",       factor_name = \"V1\",       show_counts = FALSE,       style = \"boxplot\",       jitter = FALSE,       fill = FALSE)  D = MTBLS79_DatasetExperiment C = feature_boxplot(factor_name='Species',feature_to_plot='Petal.Width') chart_plot(C,D) #> Warning: no chart defined for \"function\" #> A \"feature_boxplot\" object #> -------------------------- #> name:          Feature boxplot #> description:   A boxplot to visualise the distribution of values within a feature. #> input params:  label_outliers, feature_to_plot, factor_name, show_counts, style, jitter, fill"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_profile.html","id":null,"dir":"Reference","previous_headings":"","what":"Feature profile — feature_profile","title":"Feature profile — feature_profile","text":"plot visualising change intensity feature continuous variable time, dose, run order.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_profile.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Feature profile — feature_profile","text":"","code":"feature_profile(   run_order,   qc_label,   qc_column,   colour_by,   feature_to_plot,   plot_sd = FALSE,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_profile.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Feature profile — feature_profile","text":"run_order (character) sample-meta column name containing run order. qc_label (character) label used identify QC samples. qc_column (character) sample-meta column name containing labels used identify QC samples. colour_by (character) sample-meta column name used colour plot. feature_to_plot (numeric, character, integer) name column id plotted feature. plot_sd (logical) Plot standard deviation. Allowed values limited following: \"TRUE\": Standard deviation samples QCs included plot. \"FALSE\": Standard deviation plotted. default FALSE. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_profile.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Feature profile — feature_profile","text":"  feature_profile  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_profile.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Feature profile — feature_profile","text":"feature_profile object inherits following struct classes: [feature_profile] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_profile.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Feature profile — feature_profile","text":"","code":"M = feature_profile(       run_order = character(0),       qc_label = character(0),       qc_column = character(0),       colour_by = character(0),       feature_to_plot = numeric(0),       plot_sd = FALSE)  D = MTBLS79_DatasetExperiment() C = feature_profile(run_order='run_order',     qc_label='QC',     qc_column='Class',     colour_by='Class',     feature_to_plot=1) chart_plot(C,D) #> Warning: Removed 18 rows containing missing values (`geom_point()`)."},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_profile_array.html","id":null,"dir":"Reference","previous_headings":"","what":"Feature profile — feature_profile_array","title":"Feature profile — feature_profile_array","text":"plot visualising change intensity feature continuous variable time, dose, run order.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_profile_array.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Feature profile — feature_profile_array","text":"","code":"feature_profile_array(   run_order,   qc_label,   qc_column,   colour_by,   feature_to_plot,   nrow = 5,   log = TRUE,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_profile_array.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Feature profile — feature_profile_array","text":"run_order (character) sample-meta column name containing run order. qc_label (character) label used identify QC samples. qc_column (character) sample-meta column name containing labels used identify QC samples. colour_by (character) sample-meta column name used colour plot. feature_to_plot (numeric, character, integer) name column id plotted feature. nrow (numeric, integer) number rows plot. default 5. log (logical) Log transform. Allowed values limited following: \"TRUE\": data log tranformed plotting. \"FALSE\": data transformed plotting. default TRUE. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_profile_array.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Feature profile — feature_profile_array","text":"  feature_profile_array  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_profile_array.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Feature profile — feature_profile_array","text":"feature_profile_array object inherits following struct classes: [feature_profile_array] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_profile_array.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Feature profile — feature_profile_array","text":"","code":"M = feature_profile_array(       run_order = character(0),       qc_label = character(0),       qc_column = character(0),       colour_by = character(0),       feature_to_plot = numeric(0),       nrow = 1,       log = FALSE)  D = MTBLS79_DatasetExperiment() C = feature_profile_array(     run_order='run_order',     qc_label='QC',     qc_column='Class',     colour_by='Class',     feature_to_plot=1:3,     nrow=1,     log=TRUE) chart_plot(C,D) #> Warning: Removed 23 rows containing missing values (`geom_point()`)."},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_by_name.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter by name — filter_by_name","title":"Filter by name — filter_by_name","text":"Filter samples/variables row/column name, index logicals.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_by_name.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter by name — filter_by_name","text":"","code":"filter_by_name(mode = \"exclude\", dimension = \"sample\", names, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_by_name.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter by name — filter_by_name","text":"mode (character) filtering mode controls whether samples/features mode=\"included\" mode=\"excluded\" based name. default \"exclude\". dimension (character) filtering dimensions controls whether dimension=\"sample\" dimension=\"variable\" filtered based name. default \"sample\". names (character, numeric, logical) name features/samples filtered. Must exact match. Can also provide indexes (numeric) logical. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_by_name.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter by name — filter_by_name","text":" filter_by_name object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_by_name.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Filter by name — filter_by_name","text":"filter_by_name object inherits following struct classes: [filter_by_name] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_by_name.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Filter by name — filter_by_name","text":"","code":"M = filter_by_name(       mode = \"exclude\",       dimension = \"sample\",       names = character(0))  D = MTBLS79_DatasetExperiment() M = filter_by_name(mode='exclude',dimension='variable',names=c(1,2,3)) M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_na_count.html","id":null,"dir":"Reference","previous_headings":"","what":"Minimum number of measured values filter — filter_na_count","title":"Minimum number of measured values filter — filter_na_count","text":"number measured values counted feature, feature less predefined minimum number values group removed. several factors, threshold applied minimum number samples present combinations (interactions) groups.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_na_count.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Minimum number of measured values filter — filter_na_count","text":"","code":"filter_na_count(threshold, factor_name, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_na_count.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Minimum number of measured values filter — filter_na_count","text":"threshold (numeric) minimum number samples group/interaction. factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_na_count.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Minimum number of measured values filter — filter_na_count","text":" filter_na_count object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_na_count.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Minimum number of measured values filter — filter_na_count","text":"filter_na_count object inherits following struct classes: [filter_na_count] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_na_count.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Minimum number of measured values filter — filter_na_count","text":"","code":"M = filter_na_count(       threshold = 2,       factor_name = \"V1\")  D = MTBLS79_DatasetExperiment() M = filter_na_count(threshold=3,factor_name='Class') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_smeta.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter by sample meta data — filter_smeta","title":"Filter by sample meta data — filter_smeta","text":"data filtered named levels factor included/excluded dataset.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_smeta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter by sample meta data — filter_smeta","text":"","code":"filter_smeta(mode = \"include\", levels, factor_name, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_smeta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter by sample meta data — filter_smeta","text":"mode (character) Mode action. Allowed values limited following: \"include\": Samples specified levels retained. \"exclude\": Samples specified levels excluded. default \"include\". levels (character) level name(s) filtering. factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_smeta.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter by sample meta data — filter_smeta","text":" filter_smeta object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_smeta.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Filter by sample meta data — filter_smeta","text":"filter_smeta object inherits following struct classes: [filter_smeta] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_smeta.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Filter by sample meta data — filter_smeta","text":"","code":"M = filter_smeta(       mode = \"include\",       levels = character(0),       factor_name = \"V1\")  D = MTBLS79_DatasetExperiment() M = filter_smeta(mode='exclude',levels='QC',factor_name='QC') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fisher_exact.html","id":null,"dir":"Reference","previous_headings":"","what":"Fisher Exact Test — fisher_exact","title":"Fisher Exact Test — fisher_exact","text":"fisher exact test used analyse contingency tables comparing number correctly/incorrectly predicted group labels. multiple test corrected p-value indicates whether number measured values significantly different groups.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fisher_exact.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fisher Exact Test — fisher_exact","text":"","code":"fisher_exact(alpha = 0.05, mtc = \"fdr\", factor_name, factor_pred, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fisher_exact.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fisher Exact Test — fisher_exact","text":"alpha (numeric) p-value cutoff determining significance. default 0.05. mtc (character) Multiple test correction method. Allowed values limited following: \"bonferroni\": Bonferroni correction p-values multiplied number comparisons. \"fdr\": Benjamini Hochberg False Discovery Rate correction. \"none\": correction. default \"fdr\". factor_name (character) name sample-meta column use. factor_pred (data.frame) data.frame, column factor predicted group labels compare true groups labels. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fisher_exact.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fisher Exact Test — fisher_exact","text":" fisher_exact object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fisher_exact.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Fisher Exact Test — fisher_exact","text":"fisher_exact object inherits following struct classes: [fisher_exact] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fisher_exact.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fisher Exact Test — fisher_exact","text":"","code":"M = fisher_exact(       alpha = 0.05,       mtc = \"fdr\",       factor_name = \"V1\",       factor_pred = data.frame(id=NA))  # load some data D=MTBLS79_DatasetExperiment()  # prepare predictions based on NA pred=as.data.frame(is.na(D$data)) pred=lapply(pred,factor,levels=c(TRUE,FALSE)) pred=as.data.frame(pred)  # apply method M = fisher_exact(alpha=0.05,mtc='fdr',factor_name='Class',factor_pred=pred) M=model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change.html","id":null,"dir":"Reference","previous_headings":"","what":"Fold change — fold_change","title":"Fold change — fold_change","text":"Fold change relative change mean (non-parametric equivalent) intensities feature pairs levels factor.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fold change — fold_change","text":"","code":"fold_change(   factor_name,   paired = FALSE,   sample_name = character(0),   threshold = 2,   control_group = character(0),   method = \"geometric\",   conf_level = 0.95,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fold change — fold_change","text":"factor_name (character) name sample-meta column use. paired (logical) Paired fold change. Allowed values limited following: \"TRUE\": Fold change calculated taking account paired sampling. \"FALSE\": Fold change calculated assuming paired sampling. default FALSE. sample_name (character) name sample_meta column containing sample identifiers paired sampling. default character(0). threshold (numeric) fold change threshold labelling features significant. default 2. control_group (character) level name group used denominator (possible) computing fold change. default character(0). method (character) Fold change method. Allowed values limited following: \"geometric\": log transform applied using group means calculate fold change. non-tranformedspace equivalent using geometric group means. Confidence intervals independant paired sampling estimated using standard error mean log transformed space transformed back original space. \"median\": group medians method described Price Bonett used estimate confidence intervals. paired data standard error median used estimate confidence intervals median fold change pairs. \"mean\": group means method described Price Bonnet used estimate confidence intervals. paired data standard error mean used estimate confidence intervals mean fold change pairs. default \"geometric\". conf_level (numeric) confidence level interval. default 0.95. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fold change — fold_change","text":" fold_change object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Fold change — fold_change","text":"fold_change object inherits following struct classes: [fold_change] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fold change — fold_change","text":"Price Jr RM, Bonett DG (2020). \"Confidence Intervals Ratios Means Medians.\" Journal Educational Behavioral Statistics, 45(6), 750-770.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fold change — fold_change","text":"","code":"M = fold_change(       factor_name = \"V1\",       sample_name = character(0),       paired = FALSE,       threshold = 2,       control_group = character(0),       method = \"geometric\",       conf_level = 0.95)  D = MTBLS79_DatasetExperiment() M = fold_change(factor_name='Class') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change_int.html","id":null,"dir":"Reference","previous_headings":"","what":"Fold change for interactions between factors — fold_change_int","title":"Fold change for interactions between factors — fold_change_int","text":"one factor fold change calculation extended include combinations levels (interactions) factors. Paired fold changes possible computation.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change_int.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fold change for interactions between factors — fold_change_int","text":"","code":"fold_change_int(   factor_name,   threshold = 2,   control_group = character(0),   method = \"geometric\",   conf_level = 0.95,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change_int.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fold change for interactions between factors — fold_change_int","text":"factor_name (character) name sample-meta column use. threshold (numeric) fold change threshold labelling features significant. default 2. control_group (character) level names groups used denominator (possible) computing fold change. One level factor, assumed order factor_name. default character(0). method (character) Fold change method. Allowed values limited following: \"geometric\": log transform applied using group means calculate fold change. non-tranformedspace equivalent using geometric group means. Confidence intervals independant paired sampling estimated using standard error mean log transformed space transformed back original space. \"median\": group medians method described Price Bonett used estimate confidence intervals. paired data standard error median used estimate confidence intervals median fold change pairs. \"mean\": group means method described Price Bonnet used estimate confidence intervals. paired data standard error mean used estimate confidence intervals mean fold change pairs. default \"geometric\". conf_level (numeric) confidence level interval. default 0.95. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change_int.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fold change for interactions between factors — fold_change_int","text":" fold_change_int object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change_int.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Fold change for interactions between factors — fold_change_int","text":"fold_change_int object inherits following struct classes: [fold_change_int] >> [fold_change] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change_int.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fold change for interactions between factors — fold_change_int","text":"Lloyd GR, Jankevics , Weber RJM (2020). \"struct: R/Bioconductor-based framework standardized metabolomics data analysis beyond.\" Bioinformatics, 36(22-23), 5551-5552. https://doi.org/10.1093/bioinformatics/btaa1031.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change_int.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fold change for interactions between factors — fold_change_int","text":"","code":"M = fold_change_int(       factor_name = \"V1\",       sample_name = character(0),       threshold = 2,       control_group = character(0),       method = \"geometric\",       paired = FALSE,       conf_level = 0.95)  D = MTBLS79_DatasetExperiment() D=D[,1:10,drop=FALSE] M = filter_smeta(mode='exclude',levels='QC',factor_name='Class') +     fold_change_int(factor_name=c('Class','Batch')) M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Fold change plot — fold_change_plot","title":"Fold change plot — fold_change_plot","text":"plot fold changes calculated chosen subset features. predefined fold change threshold indicated shaded regions.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fold change plot — fold_change_plot","text":"","code":"fold_change_plot(number_features = 20, orientation = \"portrait\", ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fold change plot — fold_change_plot","text":"number_features (numeric) number randomly selected features plot, list column numbers. default 20. orientation (character) Plot orientation. Allowed values limited following: \"landscape\": Features plotted y-axis. \"portrait\": Features plotted x-axis. default \"portrait\". ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fold change plot — fold_change_plot","text":"  fold_change_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change_plot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Fold change plot — fold_change_plot","text":"fold_change_plot object inherits following struct classes: [fold_change_plot] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fold change plot — fold_change_plot","text":"","code":"M = fold_change_plot(       number_features = 10,       orientation = \"portrait\")  C = fold_change_plot()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/forward_selection_by_rank.html","id":null,"dir":"Reference","previous_headings":"","what":"Forward selection by rank — forward_selection_by_rank","title":"Forward selection by rank — forward_selection_by_rank","text":"model trained performance metric computed including increasing numbers features model. features included step defined rank, computed another variable e.g. VIP score. \"optimal\"subset features suggested minimising input performance metric.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/forward_selection_by_rank.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Forward selection by rank — forward_selection_by_rank","text":"","code":"forward_selection_by_rank(   min_no_vars = 1,   max_no_vars = 100,   step_size = 1,   factor_name,   variable_rank,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/forward_selection_by_rank.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Forward selection by rank — forward_selection_by_rank","text":"min_no_vars (numeric) minimum number variables include model. default 1. max_no_vars (numeric) maximum number variables include model. default 100. step_size (numeric) incremental change number features model. default 1. factor_name (character) name sample-meta column use. variable_rank (numeric, integer) values used rank features. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/forward_selection_by_rank.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Forward selection by rank — forward_selection_by_rank","text":" forward_selection_by_rank object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/forward_selection_by_rank.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Forward selection by rank — forward_selection_by_rank","text":"forward_selection_by_rank object inherits following struct classes: [forward_selection_by_rank] >> [resampler] >> [iterator] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/forward_selection_by_rank.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Forward selection by rank — forward_selection_by_rank","text":"","code":"M = forward_selection_by_rank(       min_no_vars = 1,       max_no_vars = 100,       step_size = 1,       factor_name = \"V1\",       variable_rank = 1)  # some data D = MTBLS79_DatasetExperiment(filtered=TRUE)  # normalise, impute and scale then remove QCs P = pqn_norm(qc_label='QC',factor_name='Class') +     knn_impute(neighbours=5) +     glog_transform(qc_label='QC',factor_name='Class') +     filter_smeta(mode='exclude',levels='QC',factor_name='Class') P = model_apply(P,D) D = predicted(P)  # forward selection using a PLSDA model M = forward_selection_by_rank(factor_name='Class',                              min_no_vars=2,                              max_no_vars=11,                              variable_rank=1:2063) *     (mean_centre() + PLSDA(number_components=1,                            factor_name='Class')) M = run(M,D,balanced_accuracy())"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fs_line.html","id":null,"dir":"Reference","previous_headings":"","what":"Forward selection line plot — fs_line","title":"Forward selection line plot — fs_line","text":"line plot forward selection. computed model performance metric plotted number features included model.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fs_line.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Forward selection line plot — fs_line","text":"","code":"fs_line(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fs_line.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Forward selection line plot — fs_line","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fs_line.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Forward selection line plot — fs_line","text":"  fs_line  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fs_line.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Forward selection line plot — fs_line","text":"fs_line object inherits following struct classes: [fs_line] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fs_line.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Forward selection line plot — fs_line","text":"","code":"M = fs_line()  # some data D = MTBLS79_DatasetExperiment(filtered=TRUE)  # normalise, impute and scale then remove QCs P = pqn_norm(qc_label='QC',factor_name='Class') +     knn_impute(neighbours=5) +     glog_transform(qc_label='QC',factor_name='Class') +     filter_smeta(mode='exclude',levels='QC',factor_name='Class') P = model_apply(P,D) D = predicted(P)  # forward selection using a PLSDA model M = forward_selection_by_rank(factor_name='Class',                              min_no_vars=2,                              max_no_vars=11,                              variable_rank=1:2063) *     (mean_centre() + PLSDA(number_components=1,                            factor_name='Class')) M = run(M,D,balanced_accuracy())  # chart C = fs_line() chart_plot(C,M)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_opt_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Glog optimisation — glog_opt_plot","title":"Glog optimisation — glog_opt_plot","text":"plot sum squares error (SSE) vs different values lambda glog transform. indicated optimum value lambda minimises SSE.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_opt_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Glog optimisation — glog_opt_plot","text":"","code":"glog_opt_plot(plot_grid = 100, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_opt_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Glog optimisation — glog_opt_plot","text":"plot_grid (numeric)  default 100. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_opt_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Glog optimisation — glog_opt_plot","text":"  glog_opt_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_opt_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Glog optimisation — glog_opt_plot","text":"object makes use functionality following packages: pmp","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_opt_plot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Glog optimisation — glog_opt_plot","text":"glog_opt_plot object inherits following struct classes: [glog_opt_plot] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_opt_plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Glog optimisation — glog_opt_plot","text":"Jankevics , Lloyd GR, Weber RJM (2023). pmp: Peak Matrix Processing signal batch correction metabolomics datasets. doi:10.18129/B9.bioc.pmp https://doi.org/10.18129/B9.bioc.pmp, R package version 1.14.0, https://bioconductor.org/packages/pmp.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_opt_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Glog optimisation — glog_opt_plot","text":"","code":"M = glog_opt_plot(       plot_grid = numeric(0))  D = iris_DatasetExperiment() M = glog_transform(qc_label='versicolor',factor_name='Species') M = model_apply(M,D) #> Error!Lambda tending to infinity! Using standard #> Error!Lambda tending to infinity! Using standard C = glog_opt_plot() chart_plot(C,M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_transform.html","id":null,"dir":"Reference","previous_headings":"","what":"Generalised logarithmic transform — glog_transform","title":"Generalised logarithmic transform — glog_transform","text":"generalised logarithm (glog) transformation applies log transformation applying offset account technical variation.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_transform.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generalised logarithmic transform — glog_transform","text":"","code":"glog_transform(qc_label = \"QC\", factor_name, lambda = NULL, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_transform.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generalised logarithmic transform — glog_transform","text":"qc_label (character) label used identify QC samples. default \"QC\". factor_name (character) name sample-meta column use. lambda (numeric, NULL) value lambda use. NULL pmp package used determine \"optimal\" value lambda. default NULL. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_transform.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generalised logarithmic transform — glog_transform","text":" glog_transform object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_transform.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generalised logarithmic transform — glog_transform","text":"object makes use functionality following packages: pmp","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_transform.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Generalised logarithmic transform — glog_transform","text":"glog_transform object inherits following struct classes: [glog_transform] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_transform.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Generalised logarithmic transform — glog_transform","text":"Jankevics , Lloyd GR, Weber RJM (2023). pmp: Peak Matrix Processing signal batch correction metabolomics datasets. doi:10.18129/B9.bioc.pmp https://doi.org/10.18129/B9.bioc.pmp, R package version 1.14.0, https://bioconductor.org/packages/pmp. Durbin B, Hardin J, Hawkins D, Rocke D (2002). \"variance-stabilizing transformation gene-expression microarray data.\" Bioinformatics, 18(Suppl 1), S105-S110. Parsons HM, Ludwig C, Gunther UL, Viant MR (2007). \"Improved classification accuracy 1- ', '2-dimensional NMR metabolomics data using variance ', 'stabilising generalised logarithm transformation.\" Bioinformatics, 8(1), 234.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_transform.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generalised logarithmic transform — glog_transform","text":"","code":"M = glog_transform(       qc_label = \"QC\",       factor_name = \"V1\",       lambda = NULL)  D = iris_DatasetExperiment() M = glog_transform(qc_label='versicolor',factor_name='Species') M = model_apply(M,D) #> Error!Lambda tending to infinity! Using standard #> Error!Lambda tending to infinity! Using standard"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/grid_search_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"One dimensional grid search — grid_search_1d","title":"One dimensional grid search — grid_search_1d","text":"one dimensional grid search calculates performance metric model evenly spaced values model input parameter. \"optimum\" value parameter suggested one maximises performance, minimises error (whichever appropriate chosen metric)","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/grid_search_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"One dimensional grid search — grid_search_1d","text":"","code":"grid_search_1d(   param_to_optimise,   search_values,   model_index,   factor_name,   max_min = \"min\",   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/grid_search_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"One dimensional grid search — grid_search_1d","text":"param_to_optimise (character) name model input parameter focus search. search_values () values input parameter optimised. model_index (numeric, integer) index model sequence uses parameter optimised. factor_name (character) name sample-meta column use. max_min (character) Maximise minimise. Allowed values limited following: \"max\": optimium parameter value suggested based maximising performance metric. \"min\": optimium parameter value suggested based minimising performance metric. default \"min\". ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/grid_search_1d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"One dimensional grid search — grid_search_1d","text":" grid_search_1d object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/grid_search_1d.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"One dimensional grid search — grid_search_1d","text":"grid_search_1d object inherits following struct classes: [grid_search_1d] >> [resampler] >> [iterator] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/grid_search_1d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"One dimensional grid search — grid_search_1d","text":"","code":"M = grid_search_1d(       param_to_optimise = character(0),       search_values = numeric(0),       model_index = numeric(0),       factor_name = \"V1\",       max_min = \"min\")  D = MTBLS79_DatasetExperiment() # some preprocessing M = pqn_norm(qc_label='QC',factor_name='Class') +     knn_impute() +     glog_transform(qc_label='QC',factor_name='Class') +     filter_smeta(factor_name='Class',levels='QC',mode='exclude') M=model_apply(M,D) D=predicted(M)  # reduce number of features for this example D=D[,1:10]  # optmise number of components for PLS model I = grid_search_1d(param_to_optimise='number_components',search_values=1:5,         model_index=2,factor_name='Class') *         (mean_centre()+PLSDA(factor_name='Class')) I = run(I,D,balanced_accuracy())"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/gs_line.html","id":null,"dir":"Reference","previous_headings":"","what":"Grid search line plot — gs_line","title":"Grid search line plot — gs_line","text":"plot calculated performance metric model input parameter values used train model. optimum parameter value indicated based minimising (maximising) chosen metric.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/gs_line.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Grid search line plot — gs_line","text":"","code":"gs_line(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/gs_line.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Grid search line plot — gs_line","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/gs_line.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Grid search line plot — gs_line","text":"  gs_line  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/gs_line.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Grid search line plot — gs_line","text":"gs_line object inherits following struct classes: [gs_line] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/gs_line.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Grid search line plot — gs_line","text":"","code":"M = gs_line()  C = gs_line()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/hca_dendrogram.html","id":null,"dir":"Reference","previous_headings":"","what":"HCA dendrogram — hca_dendrogram","title":"HCA dendrogram — hca_dendrogram","text":"dendrogram visualising clustering HCA.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/hca_dendrogram.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"HCA dendrogram — hca_dendrogram","text":"","code":"hca_dendrogram(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/hca_dendrogram.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"HCA dendrogram — hca_dendrogram","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/hca_dendrogram.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"HCA dendrogram — hca_dendrogram","text":"  hca_dendrogram  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/hca_dendrogram.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"HCA dendrogram — hca_dendrogram","text":"object makes use functionality following packages: ggdendro","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/hca_dendrogram.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"HCA dendrogram — hca_dendrogram","text":"hca_dendrogram object inherits following struct classes: [hca_dendrogram] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/hca_dendrogram.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"HCA dendrogram — hca_dendrogram","text":"de Vries , Ripley BD (2022). ggdendro: Create Dendrograms Tree Diagrams Using 'ggplot2'. R package version 0.1.23, https://CRAN.R-project.org/package=ggdendro.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/hca_dendrogram.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"HCA dendrogram — hca_dendrogram","text":"","code":"M = hca_dendrogram()  C = hca_dendrogram()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfold_xval.html","id":null,"dir":"Reference","previous_headings":"","what":"k-fold cross-validation — kfold_xval","title":"k-fold cross-validation — kfold_xval","text":"k-fold cross-validation iterative approach applied validate models. samples divided k \"folds\", subsets. subset excluded model training used model validation , resulting single left-prediction sample. Model performance metrics computed training test sets across folds.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfold_xval.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"k-fold cross-validation — kfold_xval","text":"","code":"kfold_xval(folds = 10, method = \"venetian\", factor_name, collect = NULL, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfold_xval.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"k-fold cross-validation — kfold_xval","text":"folds (numeric, integer) number cross-validation folds. default 10. method (character) Fold selection method. Allowed values limited following: \"venetian\": Every nth sample assigned fold, n number folds. \"blocks\": Blocks adjacent samples assigned fold. \"random\": Samples randomly assigned fold. default \"venetian\". factor_name (character) name sample-meta column use. collect (NULL, character) name model output collect bootstrap repetitions, addition input metric. default NULL. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfold_xval.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"k-fold cross-validation — kfold_xval","text":" kfold_xval object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfold_xval.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"k-fold cross-validation — kfold_xval","text":"kfold_xval object inherits following struct classes: [kfold_xval] >> [resampler] >> [iterator] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfold_xval.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"k-fold cross-validation — kfold_xval","text":"","code":"M = kfold_xval(       folds = 5,       method = \"random\",       factor_name = \"V1\",       collect = NULL)  D = iris_DatasetExperiment() I = kfold_xval(factor_name='Species') *     (mean_centre() + PLSDA(factor_name='Species')) I = run(I,D,balanced_accuracy())"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfoldxcv_grid.html","id":null,"dir":"Reference","previous_headings":"","what":"k-fold cross validation plot — kfoldxcv_grid","title":"k-fold cross validation plot — kfoldxcv_grid","text":"graphic visualising true class predicted class samples groups cross-validation folds.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfoldxcv_grid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"k-fold cross validation plot — kfoldxcv_grid","text":"","code":"kfoldxcv_grid(factor_name, level, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfoldxcv_grid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"k-fold cross validation plot — kfoldxcv_grid","text":"factor_name (character) name sample-meta column use. level (character) level/group plot. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfoldxcv_grid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"k-fold cross validation plot — kfoldxcv_grid","text":"  kfoldxcv_grid  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfoldxcv_grid.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"k-fold cross validation plot — kfoldxcv_grid","text":"kfoldxcv_grid object inherits following struct classes: [kfoldxcv_grid] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfoldxcv_grid.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"k-fold cross validation plot — kfoldxcv_grid","text":"","code":"M = kfoldxcv_grid(       factor_name = \"V1\",       level = \"level_1\")  D = iris_DatasetExperiment() I = kfold_xval(factor_name='Species') *     (mean_centre() + PLSDA(factor_name='Species')) I = run(I,D,balanced_accuracy())  C = kfoldxcv_grid(factor_name='Species',level='setosa') chart_plot(C,I) #> Warning: Removed 900 rows containing missing values (`geom_tile()`)."},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfoldxcv_metric.html","id":null,"dir":"Reference","previous_headings":"","what":"kfoldxcv metric plot — kfoldxcv_metric","title":"kfoldxcv metric plot — kfoldxcv_metric","text":"boxplot performance metric computed fold k-fold cross-validation.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfoldxcv_metric.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"kfoldxcv metric plot — kfoldxcv_metric","text":"","code":"kfoldxcv_metric(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfoldxcv_metric.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"kfoldxcv metric plot — kfoldxcv_metric","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfoldxcv_metric.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"kfoldxcv metric plot — kfoldxcv_metric","text":"  kfoldxcv_metric  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfoldxcv_metric.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"kfoldxcv metric plot — kfoldxcv_metric","text":"kfoldxcv_metric object inherits following struct classes: [kfoldxcv_metric] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfoldxcv_metric.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"kfoldxcv metric plot — kfoldxcv_metric","text":"","code":"M = kfoldxcv_metric()  C = kfoldxcv_metric()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/knn_impute.html","id":null,"dir":"Reference","previous_headings":"","what":"kNN missing value imputation — knn_impute","title":"kNN missing value imputation — knn_impute","text":"k-nearest neighbour missing value imputation replaces missing values data average predefined number similar neighbours value present","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/knn_impute.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"kNN missing value imputation — knn_impute","text":"","code":"knn_impute(   neighbours = 5,   sample_max = 50,   feature_max = 50,   by = \"features\",   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/knn_impute.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"kNN missing value imputation — knn_impute","text":"neighbours (numeric) number neighbours (k) use imputation. default 5. sample_max (numeric) maximum percent missing values per sample. default 50. feature_max (numeric) maximum percent missing values per feature. default 50. (character) Impute using similar \"samples\" \"features\". Default features. default \"features\". ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/knn_impute.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"kNN missing value imputation — knn_impute","text":" knn_impute object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/knn_impute.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"kNN missing value imputation — knn_impute","text":"object makes use functionality following packages: pmp","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/knn_impute.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"kNN missing value imputation — knn_impute","text":"knn_impute object inherits following struct classes: [knn_impute] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/knn_impute.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"kNN missing value imputation — knn_impute","text":"Jankevics , Lloyd GR, Weber RJM (2023). pmp: Peak Matrix Processing signal batch correction metabolomics datasets. doi:10.18129/B9.bioc.pmp https://doi.org/10.18129/B9.bioc.pmp, R package version 1.14.0, https://bioconductor.org/packages/pmp.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/knn_impute.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"kNN missing value imputation — knn_impute","text":"","code":"M = knn_impute(       neighbours = 5,       feature_max = 50,       sample_max = 50,       by = \"features\")  M = knn_impute()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kw_p_hist.html","id":null,"dir":"Reference","previous_headings":"","what":"Histogram of p values — kw_p_hist","title":"Histogram of p values — kw_p_hist","text":"histogram p-values computed kruskal-wallis method","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kw_p_hist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Histogram of p values — kw_p_hist","text":"","code":"kw_p_hist(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kw_p_hist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Histogram of p values — kw_p_hist","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kw_p_hist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Histogram of p values — kw_p_hist","text":"  kw_p_hist  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kw_p_hist.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Histogram of p values — kw_p_hist","text":"kw_p_hist object inherits following struct classes: [kw_p_hist] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kw_p_hist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Histogram of p values — kw_p_hist","text":"","code":"M = kw_p_hist()  C = kw_p_hist()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kw_rank_sum.html","id":null,"dir":"Reference","previous_headings":"","what":"Kruskal-Wallis rank sum test — kw_rank_sum","title":"Kruskal-Wallis rank sum test — kw_rank_sum","text":"Kruskal-Wallis test univariate hypothesis testing method allows multiple (n>=2) groups compared without making assumption values normally distributed. non-parametric equivalent 1-way ANOVA. test applied variables/features individually, multiple test corrected p-values computed indicate significance variables/features.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kw_rank_sum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Kruskal-Wallis rank sum test — kw_rank_sum","text":"","code":"kw_rank_sum(alpha = 0.05, mtc = \"fdr\", factor_names, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kw_rank_sum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Kruskal-Wallis rank sum test — kw_rank_sum","text":"alpha (numeric) p-value cutoff determining significance. default 0.05. mtc (character) Multiple test correction method. Allowed values limited following: \"bonferroni\": Bonferroni correction p-values multiplied number comparisons. \"fdr\": Benjamini Hochberg False Discovery Rate correction. \"none\": correction. default \"fdr\". factor_names (character) name sample meta column(s) use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kw_rank_sum.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Kruskal-Wallis rank sum test — kw_rank_sum","text":" kw_rank_sum object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kw_rank_sum.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Kruskal-Wallis rank sum test — kw_rank_sum","text":"kw_rank_sum object inherits following struct classes: [kw_rank_sum] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kw_rank_sum.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Kruskal-Wallis rank sum test — kw_rank_sum","text":"","code":"M = kw_rank_sum(       alpha = 0.05,       mtc = \"fdr\",       factor_names = \"V1\")  D = iris_DatasetExperiment() M = kw_rank_sum(factor_names='Species') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/linear_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Linear model — linear_model","title":"Linear model — linear_model","text":"Linear models can used carry regression, single stratum analysis variance analysis covariance.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/linear_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Linear model — linear_model","text":"","code":"linear_model(formula, na_action = \"na.omit\", contrasts = list(), ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/linear_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Linear model — linear_model","text":"formula (formula) symbolic description model fitted. na_action (character) NA action. Allowed values limited following: \"na.omit\": Incomplete cases removed. \"na.fail\": error thrown NA present. \"na.exclude\": Incomplete cases removed, output result padded correct size using NA. \"na.pass\": apply linear model NA present. default \"na.omit\". contrasts (list) contrasts associated factor. default list(). ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/linear_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Linear model — linear_model","text":" linear_model object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/linear_model.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Linear model — linear_model","text":"object makes use functionality following packages: stats","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/linear_model.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Linear model — linear_model","text":"linear_model object inherits following struct classes: [linear_model] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/linear_model.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Linear model — linear_model","text":"R Core Team (2023). R: Language Environment Statistical Computing. R Foundation Statistical Computing, Vienna, Austria. https://www.R-project.org/.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/linear_model.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Linear model — linear_model","text":"","code":"M = linear_model(       formula = y ~ x,       na_action = \"na.omit\",       contrasts = list())  D = iris_DatasetExperiment() M = linear_model(formula = y~Species)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/log_transform.html","id":null,"dir":"Reference","previous_headings":"","what":"logarithm transform — log_transform","title":"logarithm transform — log_transform","text":"logarithmic transform applied values data matrix.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/log_transform.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"logarithm transform — log_transform","text":"","code":"log_transform(base = 10, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/log_transform.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"logarithm transform — log_transform","text":"base (numeric) base logarithm used transform. default 10. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/log_transform.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"logarithm transform — log_transform","text":" log_transform object following output slots: struct object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/log_transform.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"logarithm transform — log_transform","text":"log_transform object inherits following struct classes: [log_transform] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/log_transform.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"logarithm transform — log_transform","text":"","code":"M = log_transform(       base = 10)  M = log_transform()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mean_centre.html","id":null,"dir":"Reference","previous_headings":"","what":"Mean centre — mean_centre","title":"Mean centre — mean_centre","text":"mean sample subtracted samples data matrix. features centred matrix zero mean.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mean_centre.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mean centre — mean_centre","text":"","code":"mean_centre(mode = \"data\", ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mean_centre.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mean centre — mean_centre","text":"mode (character) Mode action. Allowed values limited following: \"data\": Centring applied data block. \"sample_meta\": Centring applied sample_meta block. \"\": Centring applied data sample_meta blocks. default \"data\". ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mean_centre.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Mean centre — mean_centre","text":" mean_centre object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mean_centre.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Mean centre — mean_centre","text":"mean_centre object inherits following struct classes: [mean_centre] >> [preprocess] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mean_centre.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mean centre — mean_centre","text":"","code":"M = mean_centre(       mode = \"data\")  M = mean_centre()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mean_of_medians.html","id":null,"dir":"Reference","previous_headings":"","what":"Mean of medians — mean_of_medians","title":"Mean of medians — mean_of_medians","text":"data matrix normalised mean median factor level.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mean_of_medians.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mean of medians — mean_of_medians","text":"","code":"mean_of_medians(factor_name, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mean_of_medians.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mean of medians — mean_of_medians","text":"factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mean_of_medians.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Mean of medians — mean_of_medians","text":" mean_of_medians object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mean_of_medians.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Mean of medians — mean_of_medians","text":"mean_of_medians object inherits following struct classes: [mean_of_medians] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mean_of_medians.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mean of medians — mean_of_medians","text":"","code":"M = mean_of_medians(       factor_name = \"V1\")  D = iris_DatasetExperiment() M = mean_of_medians(factor_name='Species') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mixed_effect.html","id":null,"dir":"Reference","previous_headings":"","what":"Mixed effects model — mixed_effect","title":"Mixed effects model — mixed_effect","text":"mixed effects model extension ANOVA fixed random effects.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mixed_effect.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mixed effects model — mixed_effect","text":"","code":"mixed_effect(alpha = 0.05, mtc = \"fdr\", formula, ss_type = \"marginal\", ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mixed_effect.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mixed effects model — mixed_effect","text":"alpha (numeric) p-value cutoff determining significance. default 0.05. mtc (character) Multiple test correction method. Allowed values limited following: \"bonferroni\": Bonferroni correction p-values multiplied number comparisons. \"fdr\": Benjamini Hochberg False Discovery Rate correction. \"none\": correction. default \"fdr\". formula (formula) symbolic description model fitted. ss_type (character) Sum squares type. Allowed values limited following: \"marginal\": Type III sum squares. \"sequential\": Type II sum squares. default \"marginal\". ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mixed_effect.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Mixed effects model — mixed_effect","text":" mixed_effect object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mixed_effect.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Mixed effects model — mixed_effect","text":"object makes use functionality following packages: nlme emmeans","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mixed_effect.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Mixed effects model — mixed_effect","text":"mixed_effect object inherits following struct classes: [mixed_effect] >> [ANOVA] >> [model] >> [stato] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mixed_effect.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Mixed effects model — mixed_effect","text":"Pinheiro J, Bates D, R Core Team (2023). nlme: Linear Nonlinear Mixed Effects Models. R package version 3.1-164, https://CRAN.R-project.org/package=nlme. Pinheiro JC, Bates DM (2000). Mixed-Effects Models S S-PLUS. Springer, New York. doi:10.1007/b98882 https://doi.org/10.1007/b98882. Lenth R (2023). emmeans: Estimated Marginal Means, aka Least-Squares Means. R package version 1.9.0, https://CRAN.R-project.org/package=emmeans. Fox J, Weisberg S (2019). R Companion Applied Regression, Third edition. Sage, Thousand Oaks CA. https://socialsciences.mcmaster.ca/jfox/Books/Companion/.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mixed_effect.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mixed effects model — mixed_effect","text":"","code":"M = mixed_effect(       alpha = 0.05,       mtc = \"fdr\",       formula = y ~ x,       ss_type = \"marginal\")  D = iris_DatasetExperiment() D$sample_meta$id=rownames(D) # dummy id column M = mixed_effect(formula = y~Species+ Error(id/Species)) M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_apply.html","id":null,"dir":"Reference","previous_headings":"","what":"Apply method — model_apply,ANOVA,DatasetExperiment-method","title":"Apply method — model_apply,ANOVA,DatasetExperiment-method","text":"Applies method input DatasetExperiment","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_apply.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Apply method — model_apply,ANOVA,DatasetExperiment-method","text":"","code":"# S4 method for ANOVA,DatasetExperiment model_apply(M, D)  # S4 method for HSD,DatasetExperiment model_apply(M, D)  # S4 method for mixed_effect,DatasetExperiment model_apply(M, D)  # S4 method for HSDEM,DatasetExperiment model_apply(M, D)  # S4 method for classical_lsq,DatasetExperiment model_apply(M, D)  # S4 method for confounders_clsq,DatasetExperiment model_apply(M, D)  # S4 method for constant_sum_norm,DatasetExperiment model_apply(M, D)  # S4 method for corr_coef,DatasetExperiment model_apply(M, D)  # S4 method for split_data,DatasetExperiment model_apply(M, D)  # S4 method for equal_split,DatasetExperiment model_apply(M, D)  # S4 method for filter_smeta,DatasetExperiment model_apply(M, D)  # S4 method for fisher_exact,DatasetExperiment model_apply(M, D)  # S4 method for fold_change,DatasetExperiment model_apply(M, D)  # S4 method for fold_change_int,DatasetExperiment model_apply(M, D)  # S4 method for HCA,DatasetExperiment model_apply(M, D)  # S4 method for knn_impute,DatasetExperiment model_apply(M, D)  # S4 method for kw_rank_sum,DatasetExperiment model_apply(M, D)  # S4 method for log_transform,DatasetExperiment model_apply(M, D)  # S4 method for mean_of_medians,DatasetExperiment model_apply(M, D)  # S4 method for nroot_transform,DatasetExperiment model_apply(M, D)  # S4 method for pairs_filter,DatasetExperiment model_apply(M, D)  # S4 method for prop_na,DatasetExperiment model_apply(M, D)  # S4 method for rsd_filter,DatasetExperiment model_apply(M, D)  # S4 method for sb_corr,DatasetExperiment model_apply(M, D)  # S4 method for stratified_split,DatasetExperiment model_apply(M, D)  # S4 method for tSNE,DatasetExperiment model_apply(M, D)  # S4 method for ttest,DatasetExperiment model_apply(M, D)  # S4 method for vec_norm,DatasetExperiment model_apply(M, D)  # S4 method for wilcox_test,DatasetExperiment model_apply(M, D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_apply.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Apply method — model_apply,ANOVA,DatasetExperiment-method","text":"M method object D another object used first","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_apply.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Apply method — model_apply,ANOVA,DatasetExperiment-method","text":"Returns modified method object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_apply.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Apply method — model_apply,ANOVA,DatasetExperiment-method","text":"","code":"M=model() model_apply(M,DatasetExperiment()) #> Warning: no training implemented for this model #> A \"model\" object #> ---------------- #> name:           #> description:    #> predicted:      #> seq_in:        data #>"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_predict.html","id":null,"dir":"Reference","previous_headings":"","what":"Model prediction — model_predict,DFA,DatasetExperiment-method","title":"Model prediction — model_predict,DFA,DatasetExperiment-method","text":"Apply model using input DatasetExperiment. Assumes model trained first.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_predict.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Model prediction — model_predict,DFA,DatasetExperiment-method","text":"","code":"# S4 method for DFA,DatasetExperiment model_predict(M, D)  # S4 method for PCA,DatasetExperiment model_predict(M, D)  # S4 method for PLSR,DatasetExperiment model_predict(M, D)  # S4 method for PLSDA,DatasetExperiment model_predict(M, D)  # S4 method for autoscale,DatasetExperiment model_predict(M, D)  # S4 method for blank_filter,DatasetExperiment model_predict(M, D)  # S4 method for constant_sum_norm,DatasetExperiment model_predict(M, D)  # S4 method for dratio_filter,DatasetExperiment model_predict(M, D)  # S4 method for filter_by_name,DatasetExperiment model_predict(M, D)  # S4 method for filter_na_count,DatasetExperiment model_predict(M, D)  # S4 method for filter_smeta,DatasetExperiment model_predict(M, D)  # S4 method for glog_transform,DatasetExperiment model_predict(M, D)  # S4 method for linear_model,DatasetExperiment model_predict(M, D)  # S4 method for mean_centre,DatasetExperiment model_predict(M, D)  # S4 method for mv_feature_filter,DatasetExperiment model_predict(M, D)  # S4 method for mv_sample_filter,DatasetExperiment model_predict(M, D)  # S4 method for OPLSR,DatasetExperiment model_predict(M, D)  # S4 method for OPLSDA,DatasetExperiment model_predict(M, D)  # S4 method for pareto_scale,DatasetExperiment model_predict(M, D)  # S4 method for pqn_norm,DatasetExperiment model_predict(M, D)  # S4 method for SVM,DatasetExperiment model_predict(M, D)  # S4 method for vec_norm,DatasetExperiment model_predict(M, D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_predict.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Model prediction — model_predict,DFA,DatasetExperiment-method","text":"M model object D DatasetExperiment object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_predict.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Model prediction — model_predict,DFA,DatasetExperiment-method","text":"Returns modified model object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_predict.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Model prediction — model_predict,DFA,DatasetExperiment-method","text":"","code":"M = example_model() M = model_predict(M,iris_DatasetExperiment())"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_reverse.html","id":null,"dir":"Reference","previous_headings":"","what":"Reverse preprocessing — model_reverse,autoscale,DatasetExperiment-method","title":"Reverse preprocessing — model_reverse,autoscale,DatasetExperiment-method","text":"Reverse effect preprocessing step DatasetExperiment.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_reverse.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reverse preprocessing — model_reverse,autoscale,DatasetExperiment-method","text":"","code":"# S4 method for autoscale,DatasetExperiment model_reverse(M, D)  # S4 method for mean_centre,DatasetExperiment model_reverse(M, D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_reverse.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reverse preprocessing — model_reverse,autoscale,DatasetExperiment-method","text":"M model object D DatasetExperiment object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_reverse.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reverse preprocessing — model_reverse,autoscale,DatasetExperiment-method","text":"Returns modified DatasetExperiment object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_reverse.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reverse preprocessing — model_reverse,autoscale,DatasetExperiment-method","text":"","code":"M = example_model() D = model_reverse(M,iris_DatasetExperiment())"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_train.html","id":null,"dir":"Reference","previous_headings":"","what":"Train a model — model_train,DFA,DatasetExperiment-method","title":"Train a model — model_train,DFA,DatasetExperiment-method","text":"Trains model using input DatasetExperiment","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_train.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Train a model — model_train,DFA,DatasetExperiment-method","text":"","code":"# S4 method for DFA,DatasetExperiment model_train(M, D)  # S4 method for PCA,DatasetExperiment model_train(M, D)  # S4 method for PLSR,DatasetExperiment model_train(M, D)  # S4 method for PLSDA,DatasetExperiment model_train(M, D)  # S4 method for autoscale,DatasetExperiment model_train(M, D)  # S4 method for blank_filter,DatasetExperiment model_train(M, D)  # S4 method for constant_sum_norm,DatasetExperiment model_train(M, D)  # S4 method for dratio_filter,DatasetExperiment model_train(M, D)  # S4 method for filter_by_name,DatasetExperiment model_train(M, D)  # S4 method for filter_na_count,DatasetExperiment model_train(M, D)  # S4 method for filter_smeta,DatasetExperiment model_train(M, D)  # S4 method for glog_transform,DatasetExperiment model_train(M, D)  # S4 method for linear_model,DatasetExperiment model_train(M, D)  # S4 method for mean_centre,DatasetExperiment model_train(M, D)  # S4 method for mv_feature_filter,DatasetExperiment model_train(M, D)  # S4 method for mv_sample_filter,DatasetExperiment model_train(M, D)  # S4 method for OPLSR,DatasetExperiment model_train(M, D)  # S4 method for OPLSDA,DatasetExperiment model_train(M, D)  # S4 method for pareto_scale,DatasetExperiment model_train(M, D)  # S4 method for pqn_norm,DatasetExperiment model_train(M, D)  # S4 method for SVM,DatasetExperiment model_train(M, D)  # S4 method for vec_norm,DatasetExperiment model_train(M, D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_train.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Train a model — model_train,DFA,DatasetExperiment-method","text":"M model object D DatasetExperiment object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_train.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Train a model — model_train,DFA,DatasetExperiment-method","text":"Returns modified model object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_train.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Train a model — model_train,DFA,DatasetExperiment-method","text":"","code":"M = example_model() M = model_train(M,iris_DatasetExperiment())"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_boxplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Missing value boxplots — mv_boxplot","title":"Missing value boxplots — mv_boxplot","text":"Boxplots number missing values per sample/feature.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_boxplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Missing value boxplots — mv_boxplot","text":"","code":"mv_boxplot(   label_outliers = TRUE,   by_sample = TRUE,   factor_name,   show_counts = TRUE,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_boxplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Missing value boxplots — mv_boxplot","text":"label_outliers (logical) Label outliers. Allowed values limited following: \"TRUE\": Sample labels potential outliers displayed plot. \"FALSE\": Sample labels included plot. default TRUE. by_sample (logical) Plot sample feature. Allowed values limited following: \"TRUE\": Missing values plotted per sample. \"FALSE\": Missing values plotted per feature. default TRUE. factor_name (character) name sample-meta column use. show_counts (logical) Show counts. Allowed values limited following: \"TRUE\": number samples box displayed. \"FALSE\": number samples box displayed. default TRUE. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_boxplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Missing value boxplots — mv_boxplot","text":"  mv_boxplot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_boxplot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Missing value boxplots — mv_boxplot","text":"mv_boxplot object inherits following struct classes: [mv_boxplot] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_boxplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Missing value boxplots — mv_boxplot","text":"","code":"M = mv_boxplot(       label_outliers = FALSE,       by_sample = FALSE,       factor_name = \"V1\",       show_counts = FALSE)  D = MTBLS79_DatasetExperiment() C = mv_boxplot(factor_name='Class') chart_plot(C,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_feature_filter.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter features by missing values — mv_feature_filter","title":"Filter features by missing values — mv_feature_filter","text":"Removes features percentage non-missing values falls threshold.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_feature_filter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter features by missing values — mv_feature_filter","text":"","code":"mv_feature_filter(   threshold = 20,   qc_label = \"QC\",   method = \"QC\",   factor_name,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_feature_filter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter features by missing values — mv_feature_filter","text":"threshold (numeric) minimum percentage non-missing values. default 20. qc_label (character) label used identify QC/group samples using \"QC\" (within named group) filtering method. default \"QC\". method (character) Filtering method. Allowed values limited following: \"within_all\": Features removed threshold non-missing values met groups. \"within_one\": Features removed threshold non-missing values met group. \"QC\": Features removed threshold non-missing values met named group. \"across\": filter applied ignoring sample group. default \"QC\". factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_feature_filter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter features by missing values — mv_feature_filter","text":" mv_feature_filter object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_feature_filter.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Filter features by missing values — mv_feature_filter","text":"object makes use functionality following packages: pmp","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_feature_filter.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Filter features by missing values — mv_feature_filter","text":"mv_feature_filter object inherits following struct classes: [mv_feature_filter] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_feature_filter.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Filter features by missing values — mv_feature_filter","text":"Jankevics , Lloyd GR, Weber RJM (2023). pmp: Peak Matrix Processing signal batch correction metabolomics datasets. doi:10.18129/B9.bioc.pmp https://doi.org/10.18129/B9.bioc.pmp, R package version 1.14.0, https://bioconductor.org/packages/pmp.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_feature_filter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Filter features by missing values — mv_feature_filter","text":"","code":"M = mv_feature_filter(       threshold = 20,       qc_label = \"QC\",       method = \"QC\",       factor_name = \"V1\")  D = iris_DatasetExperiment() M = mv_feature_filter(factor_name='Species',qc_label='versicolor') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_feature_filter_hist.html","id":null,"dir":"Reference","previous_headings":"","what":"Histogram of missing values per feature — mv_feature_filter_hist","title":"Histogram of missing values per feature — mv_feature_filter_hist","text":"histogram proportion missing values per feature.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_feature_filter_hist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Histogram of missing values per feature — mv_feature_filter_hist","text":"","code":"mv_feature_filter_hist(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_feature_filter_hist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Histogram of missing values per feature — mv_feature_filter_hist","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_feature_filter_hist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Histogram of missing values per feature — mv_feature_filter_hist","text":"  mv_feature_filter_hist  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_feature_filter_hist.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Histogram of missing values per feature — mv_feature_filter_hist","text":"mv_feature_filter_hist object inherits following struct classes: [mv_feature_filter_hist] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_feature_filter_hist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Histogram of missing values per feature — mv_feature_filter_hist","text":"","code":"M = mv_feature_filter_hist()  C = mv_feature_filter_hist()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_histogram.html","id":null,"dir":"Reference","previous_headings":"","what":"Missing value histogram — mv_histogram","title":"Missing value histogram — mv_histogram","text":"histogram numbers missing values per sample/feature","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_histogram.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Missing value histogram — mv_histogram","text":"","code":"mv_histogram(label_outliers = TRUE, by_sample = TRUE, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_histogram.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Missing value histogram — mv_histogram","text":"label_outliers (logical) Label outliers. Allowed values limited following: \"TRUE\": Sample labels potential outliers displayed plot. \"FALSE\": Sample labels included plot. default TRUE. by_sample (logical) Plot sample feature. Allowed values limited following: \"TRUE\": Missing values plotted per sample. \"FALSE\": Missing values plotted per feature. default TRUE. ... additional slots values passed struct_class","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_histogram.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Missing value histogram — mv_histogram","text":"  mv_histogram  object. object output slots. See chart_plot struct package plot chart object. struct object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_histogram.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Missing value histogram — mv_histogram","text":"mv_histogram object inherits following struct classes: [mv_histogram] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_histogram.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Missing value histogram — mv_histogram","text":"","code":"M = mv_histogram(       label_outliers = FALSE,       by_sample = FALSE)  D = MTBLS79_DatasetExperiment() C = mv_histogram(label_outliers=FALSE,by_sample=FALSE) chart_plot(C,D) #> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`. #> Warning: Removed 2 rows containing missing values (`geom_bar()`)."},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_sample_filter.html","id":null,"dir":"Reference","previous_headings":"","what":"Missing value sample filter — mv_sample_filter","title":"Missing value sample filter — mv_sample_filter","text":"Removes samples percent number missing values exceeds threshold.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_sample_filter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Missing value sample filter — mv_sample_filter","text":"","code":"mv_sample_filter(mv_threshold = 20, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_sample_filter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Missing value sample filter — mv_sample_filter","text":"mv_threshold (numeric) maximum percentage features missing values sample. default 20. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_sample_filter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Missing value sample filter — mv_sample_filter","text":" mv_sample_filter object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_sample_filter.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Missing value sample filter — mv_sample_filter","text":"object makes use functionality following packages: pmp","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_sample_filter.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Missing value sample filter — mv_sample_filter","text":"mv_sample_filter object inherits following struct classes: [mv_sample_filter] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_sample_filter.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Missing value sample filter — mv_sample_filter","text":"Jankevics , Lloyd GR, Weber RJM (2023). pmp: Peak Matrix Processing signal batch correction metabolomics datasets. doi:10.18129/B9.bioc.pmp https://doi.org/10.18129/B9.bioc.pmp, R package version 1.14.0, https://bioconductor.org/packages/pmp.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_sample_filter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Missing value sample filter — mv_sample_filter","text":"","code":"M = mv_sample_filter(       mv_threshold = 20)  C = mv_sample_filter()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_sample_filter_hist.html","id":null,"dir":"Reference","previous_headings":"","what":"Histogram of missing values per sample — mv_sample_filter_hist","title":"Histogram of missing values per sample — mv_sample_filter_hist","text":"histogram proportion missing values per sample","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_sample_filter_hist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Histogram of missing values per sample — mv_sample_filter_hist","text":"","code":"mv_sample_filter_hist(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_sample_filter_hist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Histogram of missing values per sample — mv_sample_filter_hist","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_sample_filter_hist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Histogram of missing values per sample — mv_sample_filter_hist","text":"  mv_sample_filter_hist  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_sample_filter_hist.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Histogram of missing values per sample — mv_sample_filter_hist","text":"mv_sample_filter_hist object inherits following struct classes: [mv_sample_filter_hist] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_sample_filter_hist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Histogram of missing values per sample — mv_sample_filter_hist","text":"","code":"M = mv_sample_filter_hist()  C = mv_sample_filter_hist()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/nroot_transform.html","id":null,"dir":"Reference","previous_headings":"","what":"nth root transform — nroot_transform","title":"nth root transform — nroot_transform","text":"values data matrix transformed raising power 1/n.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/nroot_transform.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"nth root transform — nroot_transform","text":"","code":"nroot_transform(root = 2, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/nroot_transform.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"nth root transform — nroot_transform","text":"root (numeric) nth root used transform. default 2. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/nroot_transform.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"nth root transform — nroot_transform","text":" nroot_transform object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/nroot_transform.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"nth root transform — nroot_transform","text":"nroot_transform object inherits following struct classes: [nroot_transform] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/nroot_transform.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"nth root transform — nroot_transform","text":"","code":"M = nroot_transform(       root = 2)  M = nroot_transform()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ontology_cache.html","id":null,"dir":"Reference","previous_headings":"","what":"ontology cache — ontology_cache","title":"ontology cache — ontology_cache","text":"cached list ontology terms obtained ontology lookup service (OLS) ontology terms specified objects structToolbox.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ontology_cache.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ontology cache — ontology_cache","text":"","code":"ontology_cache()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ontology_cache.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"ontology cache — ontology_cache","text":"list cached ontology terms","code":""},{"path":[]},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ontology_cache.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ontology cache — ontology_cache","text":"","code":"cache = ontology_cache()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pairs_filter.html","id":null,"dir":"Reference","previous_headings":"","what":"Pairs filter — pairs_filter","title":"Pairs filter — pairs_filter","text":"filter used study designs paired sampling ensure  measurements source (e.g. patient) represented factor levels interactions.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pairs_filter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pairs filter — pairs_filter","text":"","code":"pairs_filter(factor_name, sample_id, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pairs_filter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pairs filter — pairs_filter","text":"factor_name (character) name sample-meta column use. sample_id (character) Name sample meta column containing sample identifiers. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pairs_filter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pairs filter — pairs_filter","text":" pairs_filter object following output slots: struct object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pairs_filter.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Pairs filter — pairs_filter","text":"pairs_filter object inherits following struct classes: [pairs_filter] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pairs_filter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pairs filter — pairs_filter","text":"","code":"M = pairs_filter(       factor_name = \"V1\",       sample_id = \"V1\")  M=pairs_filter(factor_name='Class',sample_id='ids')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pareto_scale.html","id":null,"dir":"Reference","previous_headings":"","what":"Pareto scaling — pareto_scale","title":"Pareto scaling — pareto_scale","text":"mean sample subtracted samples scaled square root standard deviation. transformed data zero mean.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pareto_scale.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pareto scaling — pareto_scale","text":"","code":"pareto_scale(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pareto_scale.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pareto scaling — pareto_scale","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pareto_scale.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pareto scaling — pareto_scale","text":" pareto_scale object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pareto_scale.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Pareto scaling — pareto_scale","text":"pareto_scale object inherits following struct classes: [pareto_scale] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pareto_scale.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pareto scaling — pareto_scale","text":"","code":"M = pareto_scale()  D = iris_DatasetExperiment() M = pareto_scale() M = model_train(M,D) M = model_predict(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_biplot.html","id":null,"dir":"Reference","previous_headings":"","what":"PCA biplot — pca_biplot","title":"PCA biplot — pca_biplot","text":"scatter plot selected principal component scores overlaid corresponding principal component loadings.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_biplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PCA biplot — pca_biplot","text":"","code":"pca_biplot(   components = c(1, 2),   points_to_label = \"none\",   factor_name,   scale_factor = 0.95,   style = \"points\",   label_features = FALSE,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_biplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PCA biplot — pca_biplot","text":"components (numeric) principal components used generate plot. default c(1, 2). points_to_label (character) points_to_label. Allowed values limited following: \"none\": samples labelled plot. \"\": samples labelled plot. \"outliers\": Potential outliers labelled plot. default \"none\". factor_name (character) name sample-meta column use. scale_factor (numeric) scaling factor applied loadings. default 0.95. style (character) Plot style. Allowed values limited following: \"points\": Loadings scores plotted scatter plot. \"arrows\": loadings plotted arrow vectors. default \"points\". label_features (logical) Add feature labels. Allowed values limited following: \"TRUE\": Features labelled. \"FALSE\": Features labelled. default FALSE. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_biplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PCA biplot — pca_biplot","text":"  pca_biplot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_biplot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"PCA biplot — pca_biplot","text":"pca_biplot object inherits following struct classes: [pca_biplot] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_biplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PCA biplot — pca_biplot","text":"","code":"M = pca_biplot(       components = c(1, 2),       points_to_label = \"none\",       factor_name = \"V1\",       scale_factor = 0.95,       style = \"points\",       label_features = FALSE)  C = pca_biplot(factor_name='Species')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_correlation_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"PCA correlation plot — pca_correlation_plot","title":"PCA correlation plot — pca_correlation_plot","text":"plot correlation variables/features selected principal component scores. Features high correlation well represented selected component(s)","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_correlation_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PCA correlation plot — pca_correlation_plot","text":"","code":"pca_correlation_plot(components = c(1, 2), ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_correlation_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PCA correlation plot — pca_correlation_plot","text":"components (numeric) Principal Components used generate plot. default c(1, 2). ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_correlation_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PCA correlation plot — pca_correlation_plot","text":"  pca_correlation_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_correlation_plot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"PCA correlation plot — pca_correlation_plot","text":"pca_correlation_plot object inherits following struct classes: [pca_correlation_plot] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_correlation_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PCA correlation plot — pca_correlation_plot","text":"","code":"M = pca_correlation_plot(       components = c(1, 2))  C = pca_correlation_plot()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_dstat_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"d-statistic plot — pca_dstat_plot","title":"d-statistic plot — pca_dstat_plot","text":"bar chart d-statistics samples input PCA model. Samples indicated threshold considered outlying.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_dstat_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"d-statistic plot — pca_dstat_plot","text":"","code":"pca_dstat_plot(number_components = 2, alpha = 0.05, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_dstat_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"d-statistic plot — pca_dstat_plot","text":"number_components (numeric) number principal components use. default 2. alpha (numeric) confidence threshold rejecting samples based d-statistic. default 0.05. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_dstat_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"d-statistic plot — pca_dstat_plot","text":"  pca_dstat_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_dstat_plot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"d-statistic plot — pca_dstat_plot","text":"pca_dstat_plot object inherits following struct classes: [pca_dstat_plot] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_dstat_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"d-statistic plot — pca_dstat_plot","text":"","code":"M = pca_dstat_plot(       number_components = 2,       alpha = 0.95)  C = pca_dstat_plot()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_loadings_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"PCA loadings plot — pca_loadings_plot","title":"PCA loadings plot — pca_loadings_plot","text":"barchart (one component) scatter plot (two components) selected principal component loadings.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_loadings_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PCA loadings plot — pca_loadings_plot","text":"","code":"pca_loadings_plot(   components = c(1, 2),   style = \"points\",   label_features = NULL,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_loadings_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PCA loadings plot — pca_loadings_plot","text":"components (numeric) principal components used generate plot. default c(1, 2). style (character) Plot style. Allowed values limited following: \"points\": Loadings scores plotted scatter plot. \"arrows\": loadings plotted arrow vectors. default \"points\". label_features (character, NULL) Feature labels. Allowed values limited following: \"character()\": vector labels features. \"NULL\": labels. \"row.names\": Labels extracted column names data matrix. default NULL. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_loadings_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PCA loadings plot — pca_loadings_plot","text":"  pca_loadings_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_loadings_plot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"PCA loadings plot — pca_loadings_plot","text":"pca_loadings_plot object inherits following struct classes: [pca_loadings_plot] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_loadings_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PCA loadings plot — pca_loadings_plot","text":"","code":"M = pca_loadings_plot(       components = c(1, 2),       style = \"points\",       label_features = NULL)  C = pca_loadings_plot()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_scores_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"PCA scores plot — pca_scores_plot","title":"PCA scores plot — pca_scores_plot","text":"Plots 2d scatter plot selected components","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_scores_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PCA scores plot — pca_scores_plot","text":"","code":"pca_scores_plot(   xcol = \"PC1\",   ycol = \"PC2\",   points_to_label = \"none\",   factor_name,   ellipse = \"all\",   ellipse_type = \"norm\",   ellipse_confidence = 0.95,   label_filter = character(0),   label_factor = \"rownames\",   label_size = 3.88,   components = NULL,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_scores_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PCA scores plot — pca_scores_plot","text":"xcol (numeric, integer, character) column name, index, data plot x-axis. default \"PC1\". ycol (numeric, integer, character) column name, index, data plot y-axis. default \"PC2\". points_to_label (character) Points label. Allowed values limited following: \"none\": samples labels displayed. \"\": labels samples displayed. \"outliers\": Labels potential outlier samples displayed. default \"none\". factor_name (character) name sample-meta column use. ellipse (character) Plot ellipses. Allowed values limited following: \"\": Ellipses plotted groups samples. \"group\": Ellipses plotted groups. \"none\": Ellipses included plot. \"sample\": ellipse plotted samples (ignoring group). default \"\". ellipse_type (character) Type ellipse. Allowed values limited following: \"norm\": Multivariate normal (p = 0.95). \"t\": Multivariate t (p = 0.95). default \"norm\". ellipse_confidence (numeric) confidence level plotting ellipses. default 0.95. label_filter (character) Labels plotted named groups. zero-length groups included. default character(0). label_factor (character) column name sample_meta use labelling samples plot. \"rownames\" use row names sample_meta. default \"rownames\". label_size (numeric) text size labels. Note Font Units. default 3.88. components (numeric, integer, NULL) principal components used generate plot. provided parameter overrides xcol ycol params. default NULL. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_scores_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PCA scores plot — pca_scores_plot","text":"  pca_scores_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_scores_plot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"PCA scores plot — pca_scores_plot","text":"pca_scores_plot object inherits following struct classes: [pca_scores_plot] >> [scatter_chart] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_scores_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PCA scores plot — pca_scores_plot","text":"","code":"M = pca_scores_plot(       components = NULL,       xcol = 1,       ycol = 2,       points_to_label = \"none\",       factor_name = \"V1\",       ellipse = \"all\",       label_filter = character(0),       label_factor = \"rownames\",       label_size = 3.88,       ellipse_type = \"norm\",       ellipse_confidence = 0.95)  D = iris_DatasetExperiment() M = mean_centre() + PCA() M = model_apply(M,D) C = pca_scores_plot(factor_name = 'Species') chart_plot(C,M[2])"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_scree_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Scree plot — pca_scree_plot","title":"Scree plot — pca_scree_plot","text":"plot percent variance cumulative percent variance components PCA model.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_scree_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Scree plot — pca_scree_plot","text":"","code":"pca_scree_plot(max_pc = 15, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_scree_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Scree plot — pca_scree_plot","text":"max_pc (numeric, integer) maximum number components include plot. default 15. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_scree_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Scree plot — pca_scree_plot","text":"  pca_scree_plot  object. object output slots. See chart_plot struct package plot chart object. struct object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_scree_plot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Scree plot — pca_scree_plot","text":"pca_scree_plot object inherits following struct classes: [pca_scree_plot] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_scree_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Scree plot — pca_scree_plot","text":"","code":"M = pca_scree_plot(       max_pc = 15)  C = pca_scree_plot()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permutation_test.html","id":null,"dir":"Reference","previous_headings":"","what":"Permutation test — permutation_test","title":"Permutation test — permutation_test","text":"permutation test generates \"null\" model randomising response (regression models) group labels (classification models). repeated many times generate distribution performance metrics null model. distribution can compared performance true model. overlap true null model performances model overfitted.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permutation_test.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Permutation test — permutation_test","text":"","code":"permutation_test(number_of_permutations = 50, factor_name, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permutation_test.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Permutation test — permutation_test","text":"number_of_permutations (numeric, integer) number permutations. default 50. factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permutation_test.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Permutation test — permutation_test","text":" permutation_test object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permutation_test.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Permutation test — permutation_test","text":"permutation_test object inherits following struct classes: [permutation_test] >> [resampler] >> [iterator] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permutation_test.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Permutation test — permutation_test","text":"","code":"M = permutation_test(       number_of_permutations = 100,       factor_name = \"V1\")  I=permutation_test(factor_name='Species')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permutation_test_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"permutation_test_plot class — permutation_test_plot","title":"permutation_test_plot class — permutation_test_plot","text":"Plots results permutation test.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permutation_test_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"permutation_test_plot class — permutation_test_plot","text":"","code":"permutation_test_plot(style = \"boxplot\", binwidth = 0.05, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permutation_test_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"permutation_test_plot class — permutation_test_plot","text":"style plot style. One 'boxplot', 'violin', 'histogram', 'density' 'scatter'. binwidth Binwidth \"histogram\" style. Ignored styles. ... additional slots values passed struct_class","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permutation_test_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"permutation_test_plot class — permutation_test_plot","text":"struct object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permutation_test_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"permutation_test_plot class — permutation_test_plot","text":"","code":"C = permutation_test_plot(style='boxplot')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permute_sample_order.html","id":null,"dir":"Reference","previous_headings":"","what":"Permute Sample Order — permute_sample_order","title":"Permute Sample Order — permute_sample_order","text":"order samples data matrix randomly permuted. relationship samples sample meta data maintained.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permute_sample_order.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Permute Sample Order — permute_sample_order","text":"","code":"permute_sample_order(number_of_permutations = 10, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permute_sample_order.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Permute Sample Order — permute_sample_order","text":"number_of_permutations (numeric, integer) number times sample order permuted. default 10. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permute_sample_order.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Permute Sample Order — permute_sample_order","text":" permute_sample_order object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permute_sample_order.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Permute Sample Order — permute_sample_order","text":"permute_sample_order object inherits following struct classes: [permute_sample_order] >> [resampler] >> [iterator] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permute_sample_order.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Permute Sample Order — permute_sample_order","text":"","code":"M = permute_sample_order(       number_of_permutations = 100)  C = permute_sample_order()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_regcoeff_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"pls_regcoeff_plot class — pls_regcoeff_plot","title":"pls_regcoeff_plot class — pls_regcoeff_plot","text":"Plots regression coefficients PLSDA model. Plots regression coefficient scores PLSDA model","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_regcoeff_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"pls_regcoeff_plot class — pls_regcoeff_plot","text":"","code":"pls_regcoeff_plot(ycol = 1, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_regcoeff_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"pls_regcoeff_plot class — pls_regcoeff_plot","text":"ycol (character, numeric, integer) Y column plot. default 1. ... additional slots values passed struct_class","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_regcoeff_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"pls_regcoeff_plot class — pls_regcoeff_plot","text":"  pls_regcoeff_plot  object. object output slots. See chart_plot struct package plot chart object. struct object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_regcoeff_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"pls_regcoeff_plot class — pls_regcoeff_plot","text":"object makes use functionality following packages: pls ggplot2","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_regcoeff_plot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"pls_regcoeff_plot class — pls_regcoeff_plot","text":"pls_regcoeff_plot object inherits following struct classes: [pls_regcoeff_plot] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_regcoeff_plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"pls_regcoeff_plot class — pls_regcoeff_plot","text":"Liland K, Mevik B, Wehrens R (2023). pls: Partial Least Squares Principal Component Regression. R package version 2.8-3, https://CRAN.R-project.org/package=pls. Wickham H (2016). ggplot2: Elegant Graphics Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_regcoeff_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"pls_regcoeff_plot class — pls_regcoeff_plot","text":"","code":"M = pls_regcoeff_plot(       ycol = 1)  D = iris_DatasetExperiment() M = mean_centre()+PLSDA(factor_name='Species') M = model_apply(M,D)  C = pls_regcoeff_plot(ycol='setosa') chart_plot(C,M[2])"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_scores_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"PLSDA scores plot — pls_scores_plot","title":"PLSDA scores plot — pls_scores_plot","text":"scatter plot selected PLSDA scores.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_scores_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PLSDA scores plot — pls_scores_plot","text":"","code":"pls_scores_plot(   xcol = \"LV1\",   ycol = \"LV2\",   points_to_label = \"none\",   factor_name,   ellipse = \"all\",   ellipse_type = \"norm\",   ellipse_confidence = 0.95,   label_filter = character(0),   label_factor = \"rownames\",   label_size = 3.88,   components = NULL,   ... )  plsda_scores_plot(   xcol = \"LV1\",   ycol = \"LV2\",   points_to_label = \"none\",   factor_name,   ellipse = \"all\",   ellipse_type = \"norm\",   ellipse_confidence = 0.95,   label_filter = character(0),   label_factor = \"rownames\",   label_size = 3.88,   components = NULL,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_scores_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PLSDA scores plot — pls_scores_plot","text":"xcol (numeric, integer, character) column name, index, data plot x-axis. default \"LV1\". ycol (numeric, integer, character) column name, index, data plot y-axis. default \"LV2\". points_to_label (character) Points label. Allowed values limited following: \"none\": samples labels displayed. \"\": labels samples displayed. \"outliers\": Labels potential outlier samples displayed. default \"none\". factor_name (character) name sample-meta column use. ellipse (character) Plot ellipses. Allowed values limited following: \"\": Ellipses plotted groups samples. \"group\": Ellipses plotted groups. \"none\": Ellipses included plot. \"sample\": ellipse plotted samples (ignoring group). default \"\". ellipse_type (character) Type ellipse. Allowed values limited following: \"norm\": Multivariate normal (p = 0.95). \"t\": Multivariate t (p = 0.95). default \"norm\". ellipse_confidence (numeric) confidence level plotting ellipses. default 0.95. label_filter (character) Labels plotted named groups. zero-length groups included. default character(0). label_factor (character) column name sample_meta use labelling samples plot. \"rownames\" use row names sample_meta. default \"rownames\". label_size (numeric) text size labels. Note Font Units. default 3.88. components (numeric, integer, NULL) principal components used generate plot. provided parameter overrides xcol ycol params. default NULL. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_scores_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PLSDA scores plot — pls_scores_plot","text":"  pls_scores_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_scores_plot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"PLSDA scores plot — pls_scores_plot","text":"pls_scores_plot object inherits following struct classes: [pls_scores_plot] >> [scatter_chart] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_scores_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PLSDA scores plot — pls_scores_plot","text":"","code":"M = pls_scores_plot(       components = NULL,       xcol = 1,       ycol = 2,       points_to_label = \"none\",       factor_name = \"V1\",       ellipse = \"all\",       label_filter = character(0),       label_factor = \"rownames\",       label_size = 3.88,       ellipse_type = \"norm\",       ellipse_confidence = 0.95)  D = iris_DatasetExperiment() M = mean_centre()+PLSDA(factor_name='Species') M = model_apply(M,D)  C = pls_scores_plot(factor_name='Species') chart_plot(C,M[2])"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_vip_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"PLSDA VIP plot — pls_vip_plot","title":"PLSDA VIP plot — pls_vip_plot","text":"plot Variable Importance Projection (VIP) scores PLSDA model.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_vip_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PLSDA VIP plot — pls_vip_plot","text":"","code":"pls_vip_plot(threshold = 1, ycol = 1, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_vip_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PLSDA VIP plot — pls_vip_plot","text":"threshold (numeric, integer) threshold indicating significant features. default 1. ycol (character, numeric, integer) column Y block plotted. default 1. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_vip_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PLSDA VIP plot — pls_vip_plot","text":"  pls_vip_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_vip_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PLSDA VIP plot — pls_vip_plot","text":"object makes use functionality following packages: pls ggplot2","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_vip_plot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"PLSDA VIP plot — pls_vip_plot","text":"pls_vip_plot object inherits following struct classes: [pls_vip_plot] >> [chart] >> [stato] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_vip_plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"PLSDA VIP plot — pls_vip_plot","text":"Liland K, Mevik B, Wehrens R (2023). pls: Partial Least Squares Principal Component Regression. R package version 2.8-3, https://CRAN.R-project.org/package=pls. Wickham H (2016). ggplot2: Elegant Graphics Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_vip_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PLSDA VIP plot — pls_vip_plot","text":"","code":"M = pls_vip_plot(       threshold = 1,       ycol = 1)  D = iris_DatasetExperiment() M = mean_centre()+PLSDA(factor_name='Species') M = model_apply(M,D)  C = pls_vip_plot(ycol='setosa') chart_plot(C,M[2])"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_feature_importance_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"PLSDA feature importance summary plot — plsda_feature_importance_plot","title":"PLSDA feature importance summary plot — plsda_feature_importance_plot","text":"plot selected feature significance metric PLSDA model top selected features.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_feature_importance_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PLSDA feature importance summary plot — plsda_feature_importance_plot","text":"","code":"plsda_feature_importance_plot(n_features = 30, metric = \"vip\", ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_feature_importance_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PLSDA feature importance summary plot — plsda_feature_importance_plot","text":"n_features (numeric, integer) number features include summary. default 30. metric (character) Metric plot. Allowed values limited following: \"sr\": Plot Selectivity Ratio. \"sr_pvalue\": Plot SR p-values. \"vip\": Plot Variable Importance Projection scores. default \"vip\". ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_feature_importance_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PLSDA feature importance summary plot — plsda_feature_importance_plot","text":"  plsda_feature_importance_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_feature_importance_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PLSDA feature importance summary plot — plsda_feature_importance_plot","text":"object makes use functionality following packages: pls ggplot2 reshape2 cowplot","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_feature_importance_plot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"PLSDA feature importance summary plot — plsda_feature_importance_plot","text":"plsda_feature_importance_plot object inherits following struct classes: [plsda_feature_importance_plot] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_feature_importance_plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"PLSDA feature importance summary plot — plsda_feature_importance_plot","text":"Liland K, Mevik B, Wehrens R (2023). pls: Partial Least Squares Principal Component Regression. R package version 2.8-3, https://CRAN.R-project.org/package=pls. Wickham H (2016). ggplot2: Elegant Graphics Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org. Wickham H (2007). \"Reshaping Data reshape Package.\" Journal Statistical Software, 21(12), 1-20. http://www.jstatsoft.org/v21/i12/. Wilke C (2023). cowplot: Streamlined Plot Theme Plot Annotations 'ggplot2'. R package version 1.1.2, https://CRAN.R-project.org/package=cowplot.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_feature_importance_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PLSDA feature importance summary plot — plsda_feature_importance_plot","text":"","code":"M = plsda_feature_importance_plot(       n_features = 50,       metric = \"vip\")  D = iris_DatasetExperiment() M = mean_centre()+PLSDA(factor_name='Species') M = model_apply(M,D)  C = plsda_feature_importance_plot(n_features=30,metric='vip') chart_plot(C,M[2])"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_predicted_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"PLSDA predicted plot — plsda_predicted_plot","title":"PLSDA predicted plot — plsda_predicted_plot","text":"plot regression coefficients PLSDA model.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_predicted_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PLSDA predicted plot — plsda_predicted_plot","text":"","code":"plsda_predicted_plot(factor_name, style = \"boxplot\", ycol = 1, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_predicted_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PLSDA predicted plot — plsda_predicted_plot","text":"factor_name (character) name sample-meta column use. style (character) Plot style. Allowed values limited following: \"boxplot\": boxplot. \"violin\": violin plot. \"density\": density plot. default \"boxplot\". ycol (character, numeric, integer) column Y block plotted. default 1. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_predicted_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PLSDA predicted plot — plsda_predicted_plot","text":"  plsda_predicted_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_predicted_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PLSDA predicted plot — plsda_predicted_plot","text":"object makes use functionality following packages: pls ggplot2","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_predicted_plot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"PLSDA predicted plot — plsda_predicted_plot","text":"plsda_predicted_plot object inherits following struct classes: [plsda_predicted_plot] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_predicted_plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"PLSDA predicted plot — plsda_predicted_plot","text":"Liland K, Mevik B, Wehrens R (2023). pls: Partial Least Squares Principal Component Regression. R package version 2.8-3, https://CRAN.R-project.org/package=pls. Wickham H (2016). ggplot2: Elegant Graphics Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_predicted_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PLSDA predicted plot — plsda_predicted_plot","text":"","code":"M = plsda_predicted_plot(       factor_name = \"V1\",       style = \"boxplot\",       ycol = 1)  D = iris_DatasetExperiment() M = mean_centre()+PLSDA(factor_name='Species') M = model_apply(M,D)  C = plsda_predicted_plot(factor_name='Species') chart_plot(C,M[2])"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_roc_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"PLSDA ROC plot — plsda_roc_plot","title":"PLSDA ROC plot — plsda_roc_plot","text":"Receiver Operator Characteristic (ROC) plot PLSDA models computed adjusting threshold assigning group labels PLS predictions.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_roc_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PLSDA ROC plot — plsda_roc_plot","text":"","code":"plsda_roc_plot(factor_name, ycol = 1, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_roc_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PLSDA ROC plot — plsda_roc_plot","text":"factor_name (character) name sample-meta column use. ycol (character, numeric, integer) column Y block plotted. default 1. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_roc_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PLSDA ROC plot — plsda_roc_plot","text":"  plsda_roc_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_roc_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PLSDA ROC plot — plsda_roc_plot","text":"object makes use functionality following packages: pls ggplot2","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_roc_plot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"PLSDA ROC plot — plsda_roc_plot","text":"plsda_roc_plot object inherits following struct classes: [plsda_roc_plot] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_roc_plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"PLSDA ROC plot — plsda_roc_plot","text":"Liland K, Mevik B, Wehrens R (2023). pls: Partial Least Squares Principal Component Regression. R package version 2.8-3, https://CRAN.R-project.org/package=pls. Wickham H (2016). ggplot2: Elegant Graphics Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_roc_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PLSDA ROC plot — plsda_roc_plot","text":"","code":"M = plsda_roc_plot(       factor_name = \"V1\",       ycol = 1)  D = iris_DatasetExperiment() M = mean_centre()+PLSDA(factor_name='Species') M = model_apply(M,D)  C = plsda_roc_plot(factor_name='Species') chart_plot(C,M[2])"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_cook_dist.html","id":null,"dir":"Reference","previous_headings":"","what":"Cook's distance barchart — plsr_cook_dist","title":"Cook's distance barchart — plsr_cook_dist","text":"barchart Cook's distance sample used train PLSR model. Cook's distance used estimate influence sample model can used identify potential outliers.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_cook_dist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cook's distance barchart — plsr_cook_dist","text":"","code":"plsr_cook_dist(ycol = 1, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_cook_dist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cook's distance barchart — plsr_cook_dist","text":"ycol (numeric, integer, character) y-block column plot. default 1. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_cook_dist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cook's distance barchart — plsr_cook_dist","text":"  plsr_cook_dist  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_cook_dist.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Cook's distance barchart — plsr_cook_dist","text":"plsr_cook_dist object inherits following struct classes: [plsr_cook_dist] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_cook_dist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cook's distance barchart — plsr_cook_dist","text":"","code":"M = plsr_cook_dist(       ycol = 1)  C = plsr_cook_dist()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_prediction_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"PLSR prediction plot — plsr_prediction_plot","title":"PLSR prediction plot — plsr_prediction_plot","text":"scatter plot true response values predicted values PLSR model.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_prediction_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PLSR prediction plot — plsr_prediction_plot","text":"","code":"plsr_prediction_plot(ycol = 1, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_prediction_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PLSR prediction plot — plsr_prediction_plot","text":"ycol (numeric, integer, character) y-block column plot. default 1. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_prediction_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PLSR prediction plot — plsr_prediction_plot","text":"  plsr_prediction_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_prediction_plot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"PLSR prediction plot — plsr_prediction_plot","text":"plsr_prediction_plot object inherits following struct classes: [plsr_prediction_plot] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_prediction_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PLSR prediction plot — plsr_prediction_plot","text":"","code":"M = plsr_prediction_plot(       ycol = 1)  C = plsr_prediction_plot()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_qq_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"PLSR QQ plot — plsr_qq_plot","title":"PLSR QQ plot — plsr_qq_plot","text":"plot quantiles residuals PLSR model quantiles normal distribution.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_qq_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PLSR QQ plot — plsr_qq_plot","text":"","code":"plsr_qq_plot(ycol = 1, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_qq_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PLSR QQ plot — plsr_qq_plot","text":"ycol (numeric, integer, character) y-block column plot. default 1. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_qq_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PLSR QQ plot — plsr_qq_plot","text":"  plsr_qq_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_qq_plot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"PLSR QQ plot — plsr_qq_plot","text":"plsr_qq_plot object inherits following struct classes: [plsr_qq_plot] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_qq_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PLSR QQ plot — plsr_qq_plot","text":"","code":"M = plsr_qq_plot(       ycol = 1)  C = plsr_qq_plot()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_residual_hist.html","id":null,"dir":"Reference","previous_headings":"","what":"PLSR residuals histogram — plsr_residual_hist","title":"PLSR residuals histogram — plsr_residual_hist","text":"histogram residuals PLSR model.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_residual_hist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PLSR residuals histogram — plsr_residual_hist","text":"","code":"plsr_residual_hist(ycol = 1, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_residual_hist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PLSR residuals histogram — plsr_residual_hist","text":"ycol (numeric, integer, character) y-block column plot. default 1. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_residual_hist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PLSR residuals histogram — plsr_residual_hist","text":"  plsr_residual_hist  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_residual_hist.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"PLSR residuals histogram — plsr_residual_hist","text":"plsr_residual_hist object inherits following struct classes: [plsr_residual_hist] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_residual_hist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PLSR residuals histogram — plsr_residual_hist","text":"","code":"M = plsr_residual_hist(       ycol = 1)  C = plsr_residual_hist()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pqn_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"Probabilistic Quotient Normalisation (PQN) — pqn_norm","title":"Probabilistic Quotient Normalisation (PQN) — pqn_norm","text":"PQN used normalise differences concentration samples. makes use Quality Control (QC) samples reference. PQN scales median change relative reference order robust changes caused response perturbation.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pqn_norm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Probabilistic Quotient Normalisation (PQN) — pqn_norm","text":"","code":"pqn_norm(   qc_label = \"QC\",   factor_name,   qc_frac = 0,   sample_frac = 0,   ref_method = \"mean\",   ref_mean = NULL,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pqn_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Probabilistic Quotient Normalisation (PQN) — pqn_norm","text":"qc_label (character) label used identify QC samples. default \"QC\". factor_name (character) name sample-meta column use. qc_frac (numeric) value 0 1 indicate minimum proportion QC samples feature must present included computing reference. Default qc_frac = 0. . default 0. sample_frac (numeric) value 0 1 indicate minimum proportion samples feature must present considered computing normalisation coefficients. . default 0. ref_method (character) Reference computation method. Allowed values limited following: \"mean\": reference computed mean samples matching qc_label input. \"median\": reference computed median samples matching qc_label_input. default \"mean\". ref_mean (numeric, NULL) single sample use reference normalisation. set NULL reference computed based input parameters (ref_mean, qc_label etc). . default NULL. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pqn_norm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Probabilistic Quotient Normalisation (PQN) — pqn_norm","text":" pqn_norm object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pqn_norm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Probabilistic Quotient Normalisation (PQN) — pqn_norm","text":"object makes use functionality following packages: pmp","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pqn_norm.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Probabilistic Quotient Normalisation (PQN) — pqn_norm","text":"pqn_norm object inherits following struct classes: [pqn_norm] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pqn_norm.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Probabilistic Quotient Normalisation (PQN) — pqn_norm","text":"Jankevics , Lloyd GR, Weber RJM (2023). pmp: Peak Matrix Processing signal batch correction metabolomics datasets. doi:10.18129/B9.bioc.pmp https://doi.org/10.18129/B9.bioc.pmp, R package version 1.14.0, https://bioconductor.org/packages/pmp.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pqn_norm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Probabilistic Quotient Normalisation (PQN) — pqn_norm","text":"","code":"M = pqn_norm(       qc_label = \"QC\",       factor_name = \"V1\",       qc_frac = 0,       sample_frac = 0,       ref_mean = NULL,       ref_method = \"mean\")  D = iris_DatasetExperiment() M = pqn_norm(factor_name='Species',qc_label='all') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pqn_norm_hist.html","id":null,"dir":"Reference","previous_headings":"","what":"PQN coefficient histogram — pqn_norm_hist","title":"PQN coefficient histogram — pqn_norm_hist","text":"histogram PQN coefficients features","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pqn_norm_hist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PQN coefficient histogram — pqn_norm_hist","text":"","code":"pqn_norm_hist(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pqn_norm_hist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PQN coefficient histogram — pqn_norm_hist","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pqn_norm_hist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PQN coefficient histogram — pqn_norm_hist","text":"  pqn_norm_hist  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pqn_norm_hist.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"PQN coefficient histogram — pqn_norm_hist","text":"pqn_norm_hist object inherits following struct classes: [pqn_norm_hist] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pqn_norm_hist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PQN coefficient histogram — pqn_norm_hist","text":"","code":"M = pqn_norm_hist()  C = pqn_norm_hist()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/prop_na.html","id":null,"dir":"Reference","previous_headings":"","what":"Fisher's exact test for missing values — prop_na","title":"Fisher's exact test for missing values — prop_na","text":"Fisher's exact test used compare number missing values group. Multiple test corrected p-values computed indicate whether significant difference number missing values across groups feature.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/prop_na.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fisher's exact test for missing values — prop_na","text":"","code":"prop_na(alpha = 0.05, mtc = \"fdr\", factor_name, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/prop_na.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fisher's exact test for missing values — prop_na","text":"alpha (numeric) p-value cutoff determining significance. default 0.05. mtc (character) Multiple test correction method. Allowed values limited following: \"bonferroni\": Bonferroni correction p-values multiplied number comparisons. \"fdr\": Benjamini Hochberg False Discovery Rate correction. \"none\": correction. default \"fdr\". factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/prop_na.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fisher's exact test for missing values — prop_na","text":" prop_na object following output slots: struct object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/prop_na.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Fisher's exact test for missing values — prop_na","text":"prop_na object inherits following struct classes: [prop_na] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/prop_na.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fisher's exact test for missing values — prop_na","text":"","code":"M = prop_na(       alpha = 0.05,       mtc = \"fdr\",       factor_name = \"V1\")  M = prop_na(factor_name='Species')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/r_squared.html","id":null,"dir":"Reference","previous_headings":"","what":"Coefficient of determination (R-squared) — r_squared","title":"Coefficient of determination (R-squared) — r_squared","text":"R-squared metric used assess goodness fit regression models. measures much variance one variable can explained another variable.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/r_squared.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Coefficient of determination (R-squared) — r_squared","text":"","code":"r_squared(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/r_squared.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Coefficient of determination (R-squared) — r_squared","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/r_squared.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Coefficient of determination (R-squared) — r_squared","text":"  r_squared  object. object output slots.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/r_squared.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Coefficient of determination (R-squared) — r_squared","text":"r_squared object inherits following struct classes: [r_squared] >> [metric] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/r_squared.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Coefficient of determination (R-squared) — r_squared","text":"","code":"M = r_squared()  MET = r_squared()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/resample.html","id":null,"dir":"Reference","previous_headings":"","what":"Data resampling — resample","title":"Data resampling — resample","text":"New training sets generated original data selecting samples random. can based levels factor whole dataset.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/resample.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data resampling — resample","text":"","code":"resample(   number_of_iterations = 10,   method = \"split_data\",   factor_name,   p_train = 0.8,   collect = NULL,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/resample.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Data resampling — resample","text":"number_of_iterations (numeric, integer) number training sets generate. default 10. method (character) Resampling method. Allowed values limited following: \"split_data\": Samples training set selected random full dataset. \"stratified_split\": Samples training set randomly selected level chosen factor. \"equal_split\": Samples training set selected random level main factor group sizes equal. default \"split_data\". factor_name (character) name sample-meta column use. p_train (numeric) proportion samples selected training set. default 0.8. collect (NULL, character) name model output collect bootstrap repetitions, addition input metric. default NULL. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/resample.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Data resampling — resample","text":" resample object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/resample.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Data resampling — resample","text":"resample object inherits following struct classes: [resample] >> [resampler] >> [iterator] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/resample.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data resampling — resample","text":"","code":"M = resample(       number_of_iterations = 100,       method = \"split_data\",       factor_name = \"V1\",       p_train = 0.75,       collect = NULL)  I = resample(     number_of_iterations = 10,      factor_name = 'Species',      method = 'split_data',     p_train = 0.8)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/resample_chart.html","id":null,"dir":"Reference","previous_headings":"","what":"resample_chart class — resample_chart","title":"resample_chart class — resample_chart","text":"Plots results resampling.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/resample_chart.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"resample_chart class — resample_chart","text":"","code":"resample_chart(style = \"boxplot\", binwidth = 0.05, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/resample_chart.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"resample_chart class — resample_chart","text":"style plot style. One 'boxplot', 'violin', 'histogram', 'density' 'scatter'. binwidth Binwidth \"histogram\" style. Ignored styles. ... additional slots values passed struct_class","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/resample_chart.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"resample_chart class — resample_chart","text":"struct object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/resample_chart.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"resample_chart class — resample_chart","text":"","code":"C = resample_chart(style='boxplot')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/rsd_filter.html","id":null,"dir":"Reference","previous_headings":"","what":"RSD filter — rsd_filter","title":"RSD filter — rsd_filter","text":"RSD filter calculates relative standard deviation (ratio standard deviation mean) features. feature RSD greater predefined threshold excluded.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/rsd_filter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"RSD filter — rsd_filter","text":"","code":"rsd_filter(rsd_threshold = 20, qc_label = \"QC\", factor_name, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/rsd_filter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"RSD filter — rsd_filter","text":"rsd_threshold (numeric) RSD threshold features removed. default 20. qc_label (character) label used identify QC samples. default \"QC\". factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/rsd_filter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"RSD filter — rsd_filter","text":" rsd_filter object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/rsd_filter.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"RSD filter — rsd_filter","text":"object makes use functionality following packages: pmp","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/rsd_filter.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"RSD filter — rsd_filter","text":"rsd_filter object inherits following struct classes: [rsd_filter] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/rsd_filter.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"RSD filter — rsd_filter","text":"Jankevics , Lloyd GR, Weber RJM (2023). pmp: Peak Matrix Processing signal batch correction metabolomics datasets. doi:10.18129/B9.bioc.pmp https://doi.org/10.18129/B9.bioc.pmp, R package version 1.14.0, https://bioconductor.org/packages/pmp.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/rsd_filter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"RSD filter — rsd_filter","text":"","code":"M = rsd_filter(       rsd_threshold = 20,       qc_label = \"QC\",       factor_name = \"V1\")  M = rsd_filter(factor_name='Class')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/rsd_filter_hist.html","id":null,"dir":"Reference","previous_headings":"","what":"RSD histogram — rsd_filter_hist","title":"RSD histogram — rsd_filter_hist","text":"histogram calculated RSD values.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/rsd_filter_hist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"RSD histogram — rsd_filter_hist","text":"","code":"rsd_filter_hist(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/rsd_filter_hist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"RSD histogram — rsd_filter_hist","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/rsd_filter_hist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"RSD histogram — rsd_filter_hist","text":"  rsd_filter_hist  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/rsd_filter_hist.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"RSD histogram — rsd_filter_hist","text":"rsd_filter_hist object inherits following struct classes: [rsd_filter_hist] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/rsd_filter_hist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"RSD histogram — rsd_filter_hist","text":"","code":"M = rsd_filter_hist()  C = rsd_filter_hist()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/run.html","id":null,"dir":"Reference","previous_headings":"","what":"Runs an iterator, applying the chosen model multiple times. — run,bootstrap,DatasetExperiment,metric-method","title":"Runs an iterator, applying the chosen model multiple times. — run,bootstrap,DatasetExperiment,metric-method","text":"Running iterator apply iterator number times DatasetExperiment. example, cross-validation model applied multiple times data, splitting training test sets. input metric object can calculated collected iteration output.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/run.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Runs an iterator, applying the chosen model multiple times. — run,bootstrap,DatasetExperiment,metric-method","text":"","code":"# S4 method for bootstrap,DatasetExperiment,metric run(I, D, MET = NULL)  # S4 method for forward_selection_by_rank,DatasetExperiment,metric run(I, D, MET)  # S4 method for grid_search_1d,DatasetExperiment,metric run(I, D, MET)  # S4 method for kfold_xval,DatasetExperiment,metric run(I, D, MET = NULL)  # S4 method for permutation_test,DatasetExperiment,metric run(I, D, MET = NULL)  # S4 method for permute_sample_order,DatasetExperiment,metric run(I, D, MET)  # S4 method for resample,DatasetExperiment,metric run(I, D, MET)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/run.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Runs an iterator, applying the chosen model multiple times. — run,bootstrap,DatasetExperiment,metric-method","text":"iterator object D DatasetExperiment object MET metric object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/run.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Runs an iterator, applying the chosen model multiple times. — run,bootstrap,DatasetExperiment,metric-method","text":"Modified iterator object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/run.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Runs an iterator, applying the chosen model multiple times. — run,bootstrap,DatasetExperiment,metric-method","text":"","code":"D = iris_DatasetExperiment() # get some data MET = metric()  # use a metric I = example_iterator() # initialise iterator models(I) = example_model() # set the model I = run(I,D,MET) # run #> Warning: no calculation provided for this metric"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/sb_corr.html","id":null,"dir":"Reference","previous_headings":"","what":"Signal/batch correction for mass spectrometry data — sb_corr","title":"Signal/batch correction for mass spectrometry data — sb_corr","text":"Applies Quality Control Robust Spline (QC-RSC) method correct signal drift batch differences mass spectrometry data.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/sb_corr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Signal/batch correction for mass spectrometry data — sb_corr","text":"","code":"sb_corr(   order_col,   batch_col,   qc_col,   smooth = 0,   use_log = TRUE,   min_qc = 4,   qc_label = \"QC\",   spar_lim = c(-1.5, 1.5),   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/sb_corr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Signal/batch correction for mass spectrometry data — sb_corr","text":"order_col (character) column name sample_meta indicating run order samples. batch_col (character) column name sample_meta indicating batch sample measured . qc_col (character) column name sample_meta indicating group sample member . smooth (numeric) amount smoothing applied (0 1). set 0 smoothing parameter estimated using leave-one-cross-validation. default 0. use_log (logical) Log tranformation. Allowed values limited following: \"TRUE\": data log transformed prior performing signal correction. \"FALSE\": Signal correction applied input data. default TRUE. min_qc (numeric) minimum number QC samples required signal correction. default 4. qc_label (character) label used identify QC samples. default \"QC\". spar_lim (numeric) two element vector specifying upper lower limits spar = 0. Allows value spar constrained within limits prevent overfitting. default c(-1.5, 1.5). ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/sb_corr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Signal/batch correction for mass spectrometry data — sb_corr","text":" sb_corr object following output slots: struct object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/sb_corr.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Signal/batch correction for mass spectrometry data — sb_corr","text":"object makes use functionality following packages: pmp","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/sb_corr.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Signal/batch correction for mass spectrometry data — sb_corr","text":"sb_corr object inherits following struct classes: [sb_corr] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/sb_corr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Signal/batch correction for mass spectrometry data — sb_corr","text":"Jankevics , Lloyd GR, Weber RJM (2023). pmp: Peak Matrix Processing signal batch correction metabolomics datasets. doi:10.18129/B9.bioc.pmp https://doi.org/10.18129/B9.bioc.pmp, R package version 1.14.0, https://bioconductor.org/packages/pmp. Kirwan JA, Broadhurst DI, Davidson RL, Viant MR (2013). \"Characterising correcting batch variation automated direct infusion mass spectrometry (DIMS) metabolomics workflow.\" Analytical Bioanalytical Chemistry, 405(15), 5147-5157.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/sb_corr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Signal/batch correction for mass spectrometry data — sb_corr","text":"","code":"M = sb_corr(       order_col = character(0),       batch_col = character(0),       qc_col = character(0),       smooth = 0,       use_log = FALSE,       min_qc = 4,       qc_label = \"QC\",       spar_lim = c(-1.5, 1.5))  M = sb_corr(order_col='run_order',batch_col='batch_no',qc_col='class')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/scatter_chart.html","id":null,"dir":"Reference","previous_headings":"","what":"Group scatter chart — scatter_chart","title":"Group scatter chart — scatter_chart","text":"Plots 2d scatter plot input data.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/scatter_chart.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Group scatter chart — scatter_chart","text":"","code":"scatter_chart(   xcol = 1,   ycol = 2,   points_to_label = \"none\",   factor_name = \"none\",   ellipse = \"all\",   ellipse_type = \"norm\",   ellipse_confidence = 0.95,   label_filter = character(0),   label_factor = \"rownames\",   label_size = 3.88,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/scatter_chart.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Group scatter chart — scatter_chart","text":"xcol (numeric, integer, character) column name, index, data plot x-axis. default 1. ycol (numeric, integer, character) column name, index, data plot y-axis. default 2. points_to_label (character) Points label. Allowed values limited following: \"none\": samples labels displayed. \"\": labels samples displayed. \"outliers\": Labels potential outlier samples displayed. default \"none\". factor_name (character) name sample-meta column use. default \"none\". ellipse (character) Plot ellipses. Allowed values limited following: \"\": Ellipses plotted groups samples. \"group\": Ellipses plotted groups. \"none\": Ellipses included plot. \"sample\": ellipse plotted samples (ignoring group). default \"\". ellipse_type (character) Type ellipse. Allowed values limited following: \"norm\": Multivariate normal (p = 0.95). \"t\": Multivariate t (p = 0.95). default \"norm\". ellipse_confidence (numeric) confidence level plotting ellipses. default 0.95. label_filter (character) Labels plotted named groups. zero-length groups included. default character(0). label_factor (character) column name sample_meta use labelling samples plot. \"rownames\" use row names sample_meta. default \"rownames\". label_size (numeric) text size labels. Note Font Units. default 3.88. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/scatter_chart.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Group scatter chart — scatter_chart","text":"  scatter_chart  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/scatter_chart.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Group scatter chart — scatter_chart","text":"scatter_chart object inherits following struct classes: [scatter_chart] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/scatter_chart.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Group scatter chart — scatter_chart","text":"","code":"M = scatter_chart(       xcol = 1,       ycol = 2,       points_to_label = \"none\",       factor_name = \"V1\",       ellipse = \"all\",       label_filter = character(0),       label_factor = \"rownames\",       label_size = 3.88,       ellipse_type = \"norm\",       ellipse_confidence = 0.95)  D = iris_DatasetExperiment() C = scatter_chart(         xcol = 'Petal.Width',         ycol = 'Sepal.Width',         factor_name = 'Species'     ) chart_plot(C,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/split_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Split data — split_data","title":"Split data — split_data","text":"data matrix divided two subsets.predefined proportion samples randomly selected training set, remaining samples used test set.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/split_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split data — split_data","text":"","code":"split_data(p_train, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/split_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split data — split_data","text":"p_train (numeric) proportion samples selected training set. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/split_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split data — split_data","text":" split_data object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/split_data.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Split data — split_data","text":"split_data object inherits following struct classes: [split_data] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/split_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split data — split_data","text":"","code":"M = split_data(       p_train = 0.75)  M = split_data(p_train=0.75)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/stratified_split.html","id":null,"dir":"Reference","previous_headings":"","what":"Stratified sampling — stratified_split","title":"Stratified sampling — stratified_split","text":"dataset divided two subsets. predefined proportion samples level factor selected training set, remaining samples used test set. stratification factor level means relative number samples per level approximately equal original dataset.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/stratified_split.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Stratified sampling — stratified_split","text":"","code":"stratified_split(p_train, factor_name, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/stratified_split.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Stratified sampling — stratified_split","text":"p_train (numeric) proportion samples selected training set. factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/stratified_split.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Stratified sampling — stratified_split","text":" stratified_split object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/stratified_split.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Stratified sampling — stratified_split","text":"stratified_split object inherits following struct classes: [stratified_split] >> [split_data] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/stratified_split.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Stratified sampling — stratified_split","text":"","code":"M = stratified_split(       factor_name = \"V1\",       p_train = 0.75)  D = iris_DatasetExperiment() M = stratified_split(p_train=0.75,factor_name='Species') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/structToolbox.html","id":null,"dir":"Reference","previous_headings":"","what":"structToolbox: Examples of tools built using the Statistics in R Using Class Templates (struct) package — structToolbox","title":"structToolbox: Examples of tools built using the Statistics in R Using Class Templates (struct) package — structToolbox","text":"package extends classes defined struct package","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/svm_plot_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"SVM scatter plot — svm_plot_2d","title":"SVM scatter plot — svm_plot_2d","text":"scatter plot input data group calculated boundary SVM model.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/svm_plot_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SVM scatter plot — svm_plot_2d","text":"","code":"svm_plot_2d(factor_name, npoints = 100, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/svm_plot_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"SVM scatter plot — svm_plot_2d","text":"factor_name (character) name sample-meta column use. npoints (numeric) number grid points used plot boundary. default 100. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/svm_plot_2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"SVM scatter plot — svm_plot_2d","text":"  svm_plot_2d  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/svm_plot_2d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"SVM scatter plot — svm_plot_2d","text":"object makes use functionality following packages: e1071","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/svm_plot_2d.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"SVM scatter plot — svm_plot_2d","text":"svm_plot_2d object inherits following struct classes: [svm_plot_2d] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/svm_plot_2d.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"SVM scatter plot — svm_plot_2d","text":"Meyer D, Dimitriadou E, Hornik K, Weingessel , Leisch F (2023). e1071: Misc Functions Department Statistics, Probability Theory Group (Formerly: E1071), TU Wien. R package version 1.7-14, https://CRAN.R-project.org/package=e1071.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/svm_plot_2d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"SVM scatter plot — svm_plot_2d","text":"","code":"M = svm_plot_2d(       factor_name = \"V1\",       npoints = 100)  D = iris_DatasetExperiment() M = filter_smeta(mode='exclude',levels='setosa',factor_name='Species') +     mean_centre()+PCA(number_components=2)+     SVM(factor_name='Species',kernel='linear') M = model_apply(M,D)  C = svm_plot_2d(factor_name='Species') chart_plot(C,M[4],predicted(M[3]))"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE.html","id":null,"dir":"Reference","previous_headings":"","what":"tSNE — tSNE","title":"tSNE — tSNE","text":"t-Distributed Stochastic Neighbor Embedding.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"tSNE — tSNE","text":"","code":"tSNE(   dims = 2,   perplexity = 30,   max_iter = 100,   theta = 0.5,   check_duplicates = FALSE,   init = NULL,   eta = 200,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"tSNE — tSNE","text":"dims (numeric) number tSNE dimensions computed. default 2. perplexity (numeric) Perplexity parameter. default 30. max_iter (numeric) maximum number tSNE iterations. default 100. theta (numeric) Speed/accuracy trade-. value 0 gives exact tSNE. default 0.5. check_duplicates (logical) Check duplicates. Allowed values limited following: \"TRUE\": Checks presence exact duplicate samples. \"FALSE\": check exact duplicate samples. default FALSE. init (NULL, data.frame, DatasetExperiment) set coordinates initialising tSNE algorithm. NULL uses random initialisation. default NULL. eta (numeric) learning rate parameter. default 200. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"tSNE — tSNE","text":" tSNE object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"tSNE — tSNE","text":"object makes use functionality following packages: Rtsne","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"tSNE — tSNE","text":"tSNE object inherits following struct classes: [tSNE] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"tSNE — tSNE","text":"Krijthe JH (2015). Rtsne: T-Distributed Stochastic Neighbor Embedding using Barnes-Hut Implementation. R package version 0.17, https://github.com/jkrijthe/Rtsne. van der Maaten L, Hinton G (2008). \"Visualizing High-Dimensional Data Using t-SNE.\" Journal Machine Learning Research, 9, 2579-2605. van der Maaten L (2014). \"Accelerating t-SNE using Tree-Based Algorithms.\" Journal Machine Learning Research, 15, 3221-3245.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"tSNE — tSNE","text":"","code":"M = tSNE(       dims = 2,       perplexity = 30,       max_iter = 1000,       theta = 0.5,       check_duplicates = FALSE,       init = NULL,       eta = 200)  M = tSNE()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE_scatter.html","id":null,"dir":"Reference","previous_headings":"","what":"Feature boxplot — tSNE_scatter","title":"Feature boxplot — tSNE_scatter","text":"plots new representation data applying tSNE.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE_scatter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Feature boxplot — tSNE_scatter","text":"","code":"tSNE_scatter(factor_name, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE_scatter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Feature boxplot — tSNE_scatter","text":"factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE_scatter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Feature boxplot — tSNE_scatter","text":"  tSNE_scatter  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE_scatter.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Feature boxplot — tSNE_scatter","text":"object makes use functionality following packages: Rtsne","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE_scatter.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Feature boxplot — tSNE_scatter","text":"tSNE_scatter object inherits following struct classes: [tSNE_scatter] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE_scatter.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Feature boxplot — tSNE_scatter","text":"Krijthe JH (2015). Rtsne: T-Distributed Stochastic Neighbor Embedding using Barnes-Hut Implementation. R package version 0.17, https://github.com/jkrijthe/Rtsne. van der Maaten L, Hinton G (2008). \"Visualizing High-Dimensional Data Using t-SNE.\" Journal Machine Learning Research, 9, 2579-2605. van der Maaten L (2014). \"Accelerating t-SNE using Tree-Based Algorithms.\" Journal Machine Learning Research, 15, 3221-3245.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE_scatter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Feature boxplot — tSNE_scatter","text":"","code":"M = tSNE_scatter(       factor_name = \"V1\")  M = tSNE_scatter(factor_name='Species')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tic_chart.html","id":null,"dir":"Reference","previous_headings":"","what":"Total Ion Count chart. — tic_chart","title":"Total Ion Count chart. — tic_chart","text":"scatter plot Total Ion Count (sum sample) versus run order.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tic_chart.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Total Ion Count chart. — tic_chart","text":"","code":"tic_chart(run_order, factor_name, connected = FALSE, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tic_chart.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Total Ion Count chart. — tic_chart","text":"run_order (character) column name sample_meta indicating run order samples. factor_name (character) name sample-meta column use. connected (logical) Plot samples connected grey line. default FALSE. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tic_chart.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Total Ion Count chart. — tic_chart","text":"  tic_chart  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tic_chart.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Total Ion Count chart. — tic_chart","text":"tic_chart object inherits following struct classes: [tic_chart] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tic_chart.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Total Ion Count chart. — tic_chart","text":"","code":"M = tic_chart(       factor_name = \"V1\",       run_order = character(0),       connected = FALSE)  D = iris_DatasetExperiment() D$sample_meta$run_order=1:nrow(D) C = tic_chart(factor_name='Species',run_order='run_order') chart_plot(C,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ttest.html","id":null,"dir":"Reference","previous_headings":"","what":"t-test — ttest","title":"t-test — ttest","text":"t-test compares means two factor levels. Multiple-test corrected p-values used indicate significance computed difference features.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ttest.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"t-test — ttest","text":"","code":"ttest(   alpha = 0.05,   mtc = \"fdr\",   factor_names,   paired = FALSE,   paired_factor = character(0),   equal_variance = FALSE,   conf_level = 0.95,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ttest.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"t-test — ttest","text":"alpha (numeric) p-value cutoff determining significance. default 0.05. mtc (character) Multiple test correction method. Allowed values limited following: \"bonferroni\": Bonferroni correction p-values multiplied number comparisons. \"fdr\": Benjamini Hochberg False Discovery Rate correction. \"none\": correction. default \"fdr\". factor_names (character) name sample meta column(s) use. paired (logical) Apply paired t-test. default FALSE. paired_factor (character) factor name encodes sample id pairing. default character(0). equal_variance (logical) Equal variance. Allowed values limited following: \"TRUE\": variance group treated equal using pooled variance estimate variance. \"FALSE\": variance group assumed equal Welch (Satterthwaite) approximation used. default FALSE. conf_level (numeric) confidence level interval. default 0.95. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ttest.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"t-test — ttest","text":" ttest object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ttest.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"t-test — ttest","text":"ttest object inherits following struct classes: [ttest] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ttest.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"t-test — ttest","text":"","code":"M = ttest(       alpha = 0.05,       mtc = \"fdr\",       factor_names = \"V1\",       paired = FALSE,       paired_factor = \"NA\",       equal_variance = FALSE,       conf_level = 0.95)  M = ttest(factor_name='Class')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/vec_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"Vector normalisation — vec_norm","title":"Vector normalisation — vec_norm","text":"samples data matrix normalised account differences concentration scaling sample sum squares equal 1.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/vec_norm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Vector normalisation — vec_norm","text":"","code":"vec_norm(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/vec_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Vector normalisation — vec_norm","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/vec_norm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Vector normalisation — vec_norm","text":" vec_norm object following output slots: struct object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/vec_norm.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Vector normalisation — vec_norm","text":"vec_norm object inherits following struct classes: [vec_norm] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/vec_norm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Vector normalisation — vec_norm","text":"","code":"M = vec_norm()  M = vec_norm()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/wilcox_p_hist.html","id":null,"dir":"Reference","previous_headings":"","what":"Histogram of p values — wilcox_p_hist","title":"Histogram of p values — wilcox_p_hist","text":"histogram p values wilcoxon signed rank test","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/wilcox_p_hist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Histogram of p values — wilcox_p_hist","text":"","code":"wilcox_p_hist(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/wilcox_p_hist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Histogram of p values — wilcox_p_hist","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/wilcox_p_hist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Histogram of p values — wilcox_p_hist","text":"  wilcox_p_hist  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/wilcox_p_hist.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Histogram of p values — wilcox_p_hist","text":"wilcox_p_hist object inherits following struct classes: [wilcox_p_hist] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/wilcox_p_hist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Histogram of p values — wilcox_p_hist","text":"","code":"M = wilcox_p_hist()  M = wilcox_p_hist()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/wilcox_test.html","id":null,"dir":"Reference","previous_headings":"","what":"wilcoxon signed rank test — wilcox_test","title":"wilcoxon signed rank test — wilcox_test","text":"Mann-Whitney-Wilcoxon signed rank test compares ,ranks values two groups. non-parametric equivalent t-test. Multiple test corrected p-values computed indicators significance variable/feature.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/wilcox_test.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"wilcoxon signed rank test — wilcox_test","text":"","code":"wilcox_test(   alpha = 0.05,   mtc = \"fdr\",   factor_names,   paired = FALSE,   paired_factor = character(0),   conf_level = 0.95,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/wilcox_test.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"wilcoxon signed rank test — wilcox_test","text":"alpha (numeric) p-value cutoff determining significance. default 0.05. mtc (character) Multiple test correction method. Allowed values limited following: \"bonferroni\": Bonferroni correction p-values multiplied number comparisons. \"fdr\": Benjamini Hochberg False Discovery Rate correction. \"none\": correction. default \"fdr\". factor_names (character) name sample-meta column use. paired (logical) Apply paired test. default FALSE. paired_factor (character) factor name containing sample ids paired data. default character(0). conf_level (numeric) confidence level interval. default 0.95. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/wilcox_test.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"wilcoxon signed rank test — wilcox_test","text":" wilcox_test object following output slots: struct object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/wilcox_test.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"wilcoxon signed rank test — wilcox_test","text":"wilcox_test object inherits following struct classes: [wilcox_test] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/wilcox_test.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"wilcoxon signed rank test — wilcox_test","text":"","code":"M = wilcox_test(       alpha = 0.05,       mtc = \"fdr\",       factor_names = \"V1\",       paired = FALSE,       paired_factor = character(0),       conf_level = 0.95)  M = wilcox_test(factor_name='Class')"}]
+[{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"‘structToolbox’ includes extensive set data (pre-)processing analysis tools metabolomics omics, strong emphasis statistics machine learning. methods tools implemented using class-based templates available via struct (Statistics R Using Class-based Templates) package. aim vignette introduce reader basic advanced structToolbox-based operations implementations, use struct objects, getting/setting methods/parameters, building workflows analysis mass spectrometry (MS) nuclear magnetic resonance (NMR)-based Metabolomics proteomics datasets. workflows demonstrated include wide range methods tools including pre-processing filtering, normalisation scaling, followed univariate /multivariate statistics, machine learning approaches.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"getting-started","dir":"Articles","previous_headings":"","what":"Getting started","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"latest version structToolbox compatible current R version can installed using BiocManager. number additional packages needed vignette.","code":"# install BiocManager if not present if (!requireNamespace(\"BiocManager\", quietly = TRUE))     install.packages(\"BiocManager\")  # install structToolbox and dependencies BiocManager::install(\"structToolbox\") ## install additional bioc packages for vignette if needed #BiocManager::install(c('pmp', 'ropls', 'BiocFileCache'))  ## install additional CRAN packages if needed #install.packages(c('cowplot', 'openxlsx'))  suppressPackageStartupMessages({     # Bioconductor packages     library(structToolbox)     library(pmp)     library(ropls)     library(BiocFileCache)        # CRAN libraries     library(ggplot2)     library(gridExtra)     library(cowplot)     library(openxlsx) })   # use the BiocFileCache bfc <- BiocFileCache(ask = FALSE)"},{"path":[]},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"introduction-1","dir":"Articles","previous_headings":"Introduction to struct objects, including models, model sequences, model charts and ontology.","what":"Introduction","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"PCA (Principal Component Analysis) PLS (Partial Least Squares) commonly applied methods exploring analysing multivariate datasets. use two statistical methods demonstrate different types struct (STatistics R Using Class Templates) objects available part structToolbox objects (.e. class templates) can used conduct unsupervised supervised multivariate statistical analysis.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"dataset","dir":"Articles","previous_headings":"Introduction to struct objects, including models, model sequences, model charts and ontology.","what":"Dataset","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"demonstration purposes use “Iris” dataset. famous (Fisher’s Anderson’s) dataset contains measurements sepal length width petal length width, centimeters, 50 flowers 3 class Iris. class Iris setosa, versicolor, virginica. See (https://stat.ethz.ch/R-manual/R-devel/library/datasets/html/iris.html) information. Note: vignette also compatible Direct infusion mass spectrometry metabolomics “benchmark” dataset described Kirwan et al., Sci Data 1, 140012 (2014) (https://doi.org/10.1038/sdata.2014.12). datasets available part structToolbox package already prepared DatasetExperiment object.","code":"## Iris dataset (comment if using MTBLS79 benchmark data) D = iris_DatasetExperiment() D$sample_meta$class = D$sample_meta$Species  ## MTBLS (comment if using Iris data) # D = MTBLS79_DatasetExperiment(filtered=TRUE) # M = pqn_norm(qc_label='QC',factor_name='sample_type') +  #   knn_impute(neighbours=5) + #   glog_transform(qc_label='QC',factor_name='sample_type') + #   filter_smeta(mode='exclude',levels='QC',factor_name='sample_type') # M = model_apply(M,D) # D = predicted(M)  # show info D ## A \"DatasetExperiment\" object ## ---------------------------- ## name:          Fisher's Iris dataset ## description:   This famous (Fisher's or Anderson's) iris data set gives the measurements in centimeters of ##                  the variables sepal length and width and petal length and width, ##                  respectively, for 50 flowers from each of 3 species of iris. The species are ##                  Iris setosa, versicolor, and virginica. ## data:          150 rows x 4 columns ## sample_meta:   150 rows x 2 columns ## variable_meta: 4 rows x 1 columns"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"datasetexperiment-objects","dir":"Articles","previous_headings":"Introduction to struct objects, including models, model sequences, model charts and ontology. > Dataset","what":"DatasetExperiment objects","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"DatasetExperiment object extension SummarizedExperiment class used Bioconductor community. contains three main parts: data data frame containing measured data sample. sample_meta data frame additional information related samples e.g. group labels. variable_meta data frame additional information related variables (features) e.g. annotations Like struct objects also contains name description fields (called “slots” R language). key difference DatasetExperiment SummarizedExperiment objects data transposed. .e. DatasetExperiment objects samples rows features columns, opposite true SummarizedExperiment objects. slots accessible using dollar notation.","code":"# show some data head(D$data[,1:4]) ##   Sepal.Length Sepal.Width Petal.Length Petal.Width ## 1          5.1         3.5          1.4         0.2 ## 2          4.9         3.0          1.4         0.2 ## 3          4.7         3.2          1.3         0.2 ## 4          4.6         3.1          1.5         0.2 ## 5          5.0         3.6          1.4         0.2 ## 6          5.4         3.9          1.7         0.4"},{"path":[]},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"statistical-models","dir":"Articles","previous_headings":"Introduction to struct objects, including models, model sequences, model charts and ontology. > Using struct model objects","what":"Statistical models","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"can apply e.g. PCA first need create PCA object. object contains inputs, outputs methods needed apply PCA. can set parameters number components PCA model created, can also use dollar notation change/view later. inputs model can listed using param_ids(object): summary object can printed console:","code":"P = PCA(number_components=15) P$number_components=5 P$number_components ## [1] 5 param_ids(P) ## [1] \"number_components\" P ## A \"PCA\" object ## -------------- ## name:          Principal Component Analysis (PCA) ## description:   PCA is a multivariate data reduction technique. It summarises the data in a smaller number of ##                  Principal Components that maximise variance. ## input params:  number_components  ## outputs:       scores, loadings, eigenvalues, ssx, correlation, that  ## predicted:     that ## seq_in:        data"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"model-sequences","dir":"Articles","previous_headings":"Introduction to struct objects, including models, model sequences, model charts and ontology. > Using struct model objects","what":"Model sequences","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"Unless good reason , usually sensible mean centre columns data PCA. Using STRUCT framework can create model sequence mean centre apply PCA mean centred data. structToolbox mean centring PCA model objects, joining using “+” creates model_sequence object. model_sequence outputs first object (mean centring) automatically passed inputs second object (PCA), allows chain together modelling steps order build workflow. objects model_sequence can accessed indexing, can combine dollar notation. example, PCA object second object sequence can access number components follows:","code":"M = mean_centre() + PCA(number_components = 4) M[2]$number_components ## [1] 4"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"trainingtesting-models","dir":"Articles","previous_headings":"Introduction to struct objects, including models, model sequences, model charts and ontology. > Using struct model objects","what":"Training/testing models","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"Model model_sequence objects need trained using data form DatasetExperiment object. example, PCA model sequence created (M) can trained using iris DatasetExperiment object (‘D’). model sequence now mean centred original data calculated PCA scores loadings. Model objects can used generate predictions test datasets. PCA model sequence involves mean centring test data using mean training data, projecting centred test data onto PCA model using loadings. outputs stored model sequence can accessed using dollar notation. example just use training data (sometimes called autoprediction), PCA allows us explore training data detail. Sometimes models don’t make use training/test approach e.g. univariate statsitics, filtering etc. models model_apply method can used instead. models provide training/test methods, model_apply applies autoprediction default .e. short-cut applying model_train model_predict data. available outputs object can listed accessed like input params, using dollar notation:","code":"M = model_train(M,D) M = model_predict(M,D) M = model_apply(M,D) output_ids(M[2]) ## [1] \"scores\"      \"loadings\"    \"eigenvalues\" \"ssx\"         \"correlation\" ## [6] \"that\" M[2]$scores ## A \"DatasetExperiment\" object ## ---------------------------- ## name:           ## description:    ## data:          150 rows x 4 columns ## sample_meta:   150 rows x 2 columns ## variable_meta: 4 rows x 1 columns"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"model-charts","dir":"Articles","previous_headings":"Introduction to struct objects, including models, model sequences, model charts and ontology. > Using struct model objects","what":"Model charts","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"struct framework includes chart objects. Charts associated model object can listed. Like model objects, chart objects need created can used. plot PCA scores plot mean centred PCA model.  Note indexing PCA model required pca_scores_plot object requires PCA object input, model_sequence. make changes input parameters chart, chart_plot must called see effects.  chart_plot method returns ggplot object can easily combine plots using gridExtra cowplot packages example.","code":"chart_names(M[2]) ## [1] \"pca_biplot\"           \"pca_correlation_plot\" \"pca_dstat_plot\"       ## [4] \"pca_loadings_plot\"    \"pca_scores_plot\"      \"pca_scree_plot\" C = pca_scores_plot(factor_name='class') # colour by class chart_plot(C,M[2]) # add petal width to meta data of pca scores M[2]$scores$sample_meta$example=D$data[,1] # update plot C$factor_name='example' chart_plot(C,M[2]) # scores plot C1 = pca_scores_plot(factor_name='class') # colour by class g1 = chart_plot(C1,M[2])  # scree plot C2 = pca_scree_plot() g2 = chart_plot(C2,M[2])  # arange in grid grid.arrange(grobs=list(g1,g2),nrow=1)"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"ontology","dir":"Articles","previous_headings":"Introduction to struct objects, including models, model sequences, model charts and ontology. > Using struct model objects","what":"Ontology","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"Within struct framework (structToolbox) ontology slot provided allow standardardised definitions objects inputs outputs using Ontology Lookup Service (OLS). example, STATO general purpose STATistics Ontology (http://stato-ontology.org). webpage: aim provide coverage processes statistical tests, conditions application, information needed resulting statistical methods, probability distributions, variables, spread variation metrics. STATO also covers aspects experimental design description plots graphical representations commonly used provide visual cues data distribution layout assist review results. ontology object can set assigning ontology term identifier ontology slot struct_class object design time. ids can listed using $ notation: ontology method can used obtain detailed ontology information. cache = NULL struct package automatically attempt use OLS API (via rols package) obtain name description provided identifiers. used cached versions ontology definitions provided structToolbox package prevent issues connecting OLS API building package. Note ontology method returns definitions object (PCA) inputs/outputs (number_of_components).","code":"# create an example PCA object P=PCA()  # ontology for the PCA object P$ontology ## [1] \"OBI:0200051\" ontology(P,cache = ontology_cache()) # set cache = NULL (default) for online use ## [[1]] ## An object of class \"ontology_list\" ## Slot \"terms\": ## [[1]] ## term id:       OBI:0200051 ## ontology:      obi ## label:         principal components analysis dimensionality reduction ## description:   A principal components analysis dimensionality reduction is a dimensionality reduction ##                  achieved by applying principal components analysis and by keeping low-order principal ##                  components and excluding higher-order ones. ## iri:           http://purl.obolibrary.org/obo/OBI_0200051 ##  ##  ##  ## [[2]] ## An object of class \"ontology_list\" ## Slot \"terms\": ## [[1]] ## term id:       STATO:0000555 ## ontology:      stato ## label:         number of predictive components ## description:   number of predictive components is a count used as input to the principle component analysis ##                  (PCA) ## iri:           http://purl.obolibrary.org/obo/STATO_0000555"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"validating-supervised-statistical-models","dir":"Articles","previous_headings":"Introduction to struct objects, including models, model sequences, model charts and ontology.","what":"Validating supervised statistical models","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"Validation important aspect chemometric modelling. struct framework enables kind iterative model testing iterator objects.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"cross-validation","dir":"Articles","previous_headings":"Introduction to struct objects, including models, model sequences, model charts and ontology. > Validating supervised statistical models","what":"Cross-validation","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"Cross validation common technique assessing performance classification models. example use Partial least squares-discriminant analysis (PLS-DA) model. Data mean centred prior PLS, build model sequence first. iterator objects like k-fold cross-validation object (kfold_xval) can created just like struct object. Parameters can set creation using equals sign, accessed changed later using dollar notation. model cross-validated can set/accessed using models method. Alternatively, iterators can combined models using multiplication symbol shorthand models assignement method: run method can used iterator object. iterator run set model model sequence multiple times. case run cross-validation 5 times, splitting data different training test sets time. run method also needs metric specified, another type struct object. metric may calculated iterations, iteration, depending iterator type (resampling, permutation etc). cross-validation calculate “balanced accuracy” iterations. Note balanced_accuracy metric actually reports 1-accuracy, value 0 indicates perfect performance. standard deviation “sd” NA example one permutation. Like struct objects, iterators can chart objects associated . chart_names function list object. Charts iterator objects can plotted way charts object.  possible combine multiple iterators using multiplication symbol. equivalent nesting one iterator inside . example, can repeat cross-validation multiple times permuting sample order.","code":"M = mean_centre() + PLSDA(number_components=2,factor_name='class') M ## A model_seq object containing: ##  ## [1] ## A \"mean_centre\" object ## ---------------------- ## name:          Mean centre ## description:   The mean sample is subtracted from all samples in the data matrix. The features in the centred ##                  matrix all have zero mean. ## input params:  mode  ## outputs:       centred, mean_data, mean_sample_meta  ## predicted:     centred ## seq_in:        data ##  ## [2] ## A \"PLSDA\" object ## ---------------- ## name:          Partial least squares discriminant analysis ## description:   PLS is a multivariate regression technique that extracts latent variables maximising ##                  covariance between the input data and the response. The Discriminant Analysis ##                  variant uses group labels in the response variable. For >2 groups a 1-vs-all ##                  approach is used. Group membership can be predicted for test samples based on ##                  a probability estimate of group membership, or the estimated y-value. ## input params:  number_components, factor_name, pred_method  ## outputs:       scores, loadings, yhat, design_matrix, y, reg_coeff, probability, vip, pls_model, pred, threshold, sr, sr_pvalue  ## predicted:     pred ## seq_in:        data # create object XCV = kfold_xval(folds=5,factor_name='class')  # change the number of folds XCV$folds=10 XCV$folds ## [1] 10 models(XCV)=M models(XCV) ## A model_seq object containing: ##  ## [1] ## A \"mean_centre\" object ## ---------------------- ## name:          Mean centre ## description:   The mean sample is subtracted from all samples in the data matrix. The features in the centred ##                  matrix all have zero mean. ## input params:  mode  ## outputs:       centred, mean_data, mean_sample_meta  ## predicted:     centred ## seq_in:        data ##  ## [2] ## A \"PLSDA\" object ## ---------------- ## name:          Partial least squares discriminant analysis ## description:   PLS is a multivariate regression technique that extracts latent variables maximising ##                  covariance between the input data and the response. The Discriminant Analysis ##                  variant uses group labels in the response variable. For >2 groups a 1-vs-all ##                  approach is used. Group membership can be predicted for test samples based on ##                  a probability estimate of group membership, or the estimated y-value. ## input params:  number_components, factor_name, pred_method  ## outputs:       scores, loadings, yhat, design_matrix, y, reg_coeff, probability, vip, pls_model, pred, threshold, sr, sr_pvalue  ## predicted:     pred ## seq_in:        data # cross validation of a mean centred PLSDA model XCV = kfold_xval(         folds=5,         method='venetian',         factor_name='class') *        (mean_centre() + PLSDA(factor_name='class')) XCV = run(XCV,D,balanced_accuracy()) XCV$metric ##              metric mean sd ## 1 balanced_accuracy 0.11 NA chart_names(XCV) ## [1] \"kfoldxcv_grid\"   \"kfoldxcv_metric\" C = kfoldxcv_grid(   factor_name='class',   level=levels(D$sample_meta$class)[2]) # first level chart_plot(C,XCV) # permute sample order 10 times and run cross-validation P = permute_sample_order(number_of_permutations = 10) *      kfold_xval(folds=5,factor_name='class')*     (mean_centre() + PLSDA(factor_name='class',number_components=2)) P = run(P,D,balanced_accuracy()) P$metric ##              metric   mean          sd ## 1 balanced_accuracy 0.1095 0.004972145"},{"path":[]},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"introduction-2","dir":"Articles","previous_headings":"A typical workflow for processing and analysing mass spectrometry-based metabolomics data.","what":"Introduction","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"vignette provides overview structToolbox workflow implemented process (e.g. filter features, signal drift batch correction, normalise missing value imputation) mass spectrometry data. workflow exists methods part Peak Matrix Processing (pmp) package, including range additional filters described Kirwan et al., 2013, 2014. packages required vignette addition structToolbox:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"dataset-1","dir":"Articles","previous_headings":"A typical workflow for processing and analysing mass spectrometry-based metabolomics data.","what":"Dataset","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"demonstration purposes process analyse MTBLS79 dataset (‘Dataset 7:SFPM’ Kirwan et al., 2014. dataset represents systematic evaluation reproducibility multi-batch direct-infusion mass spectrometry (DIMS)-based metabolomics study cardiac tissue extracts. comprises twenty biological samples (cow vs. sheep) analysed repeatedly, 8 batches across 7 days, together concurrent set quality control (QC) samples. Data presented step data processing workflow available MetaboLights (https://www.ebi.ac.uk/metabolights/MTBLS79). MTBLS79_DatasetExperiment object included structToolbox package processed version MTBLS79 dataset available peak matrix processing (pmp) package. vignette describes step step structToolbox version created pmp version (.e. ‘Dataset 7:SFPM’ Scientific Data publication - https://doi.org/10.1038/sdata.2014.12). SummarizedExperiment object pmp package needs converted DatasetExperiment object use structToolbox. Full processing data set requires number steps. applied using single struct model sequence (model_seq).","code":"# the pmp SE object SE = MTBLS79  # convert to DE DE = as.DatasetExperiment(SE) DE$name = 'MTBLS79' DE$description = 'Converted from SE provided by the pmp package'  # add a column indicating the order the samples were measured in DE$sample_meta$run_order = 1:nrow(DE)  # add a column indicating if the sample is biological or a QC Type=as.character(DE$sample_meta$Class) Type[Type != 'QC'] = 'Sample' DE$sample_meta$Type = factor(Type)  # add a column for plotting batches DE$sample_meta$batch_qc = DE$sample_meta$Batch DE$sample_meta$batch_qc[DE$sample_meta$Type=='QC']='QC'  # convert to factors DE$sample_meta$Batch = factor(DE$sample_meta$Batch) DE$sample_meta$Type = factor(DE$sample_meta$Type) DE$sample_meta$Class = factor(DE$sample_meta$Class) DE$sample_meta$batch_qc = factor(DE$sample_meta$batch_qc)  # print summary DE ## A \"DatasetExperiment\" object ## ---------------------------- ## name:          MTBLS79 ## description:   Converted from SE provided by the pmp package ## data:          172 rows x 2488 columns ## sample_meta:   172 rows x 7 columns ## variable_meta: 2488 rows x 0 columns"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"signal-drift-and-batch-correction","dir":"Articles","previous_headings":"A typical workflow for processing and analysing mass spectrometry-based metabolomics data.","what":"Signal drift and batch correction","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"batch correction algorithm applied reduce intra- inter- batch variations dataset. Quality Control-Robust Spline Correction (QC-RSC) provided pmp package, wrapped structToolbox object called sb_corr. figure shows plot feature vs run order, correction. fitted spline batch shown grey. can seen correction removed instrument drift within batches.   additional step added published workflow remove feature corrected QCRCMS. can occur enough measured QC values within batch. QCRMS pmp package currently returns NA samples feature occurs. Features occurs excluded. output step output MTBLS79_DatasetExperiment(filtered=FALSE).","code":"M = # batch correction     sb_corr(       order_col='run_order',       batch_col='Batch',        qc_col='Type',        qc_label='QC',       spar_lim = c(0.6,0.8)      )  M = model_apply(M,DE) C = feature_profile(       run_order='run_order',       qc_label='QC',       qc_column='Type',       colour_by='batch_qc',       feature_to_plot='200.03196',       plot_sd=FALSE   )  # plot and modify using ggplot2  chart_plot(C,M,DE)+ylab('Peak area')+ggtitle('Before') chart_plot(C,predicted(M))+ylab('Peak area')+ggtitle('After') M2 = filter_na_count(       threshold=3,       factor_name='Batch'     ) M2 = model_apply(M2,predicted(M))  # calculate number of features removed nc = ncol(DE) - ncol(predicted(M2))  cat(paste0('Number of features removed: ', nc)) ## Number of features removed: 425"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"feature-filtering","dir":"Articles","previous_headings":"A typical workflow for processing and analysing mass spectrometry-based metabolomics data.","what":"Feature filtering","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"journal article three spectral cleaning steps applied. first filter Kruskal-Wallis test used identify features reliably detected QC samples (p < 0.0001) batches. follow parameters original article use multiple test correction (mtc = 'none'). make use univariate tests kw_rank_sum filter advanced features struct needed. Slots predicted, seq_in used ensure correct output univariate test connected correct input feature filter using filter_by_name. Another slot seq_fcn used extract relevant column predicted output compatible seq_in input. placeholder used “names” parameter (names = 'place_holder') input replaced output seq_fcn. second filter Wilcoxon Signed-Rank test. used identify features representative average biological samples (p < 1e-14). make use seq_in seq_fcn. Finally, RSD filter used remove features high analytical variation (QC RSD > 20 removed) output filter output MTBLS79_DatasetExperiment(filtered=TRUE).","code":"M3 = kw_rank_sum(       alpha=0.0001,       mtc='none',       factor_names='Batch',       predicted='significant'     ) +     filter_by_name(       mode='exclude',       dimension = 'variable',       seq_in = 'names',        names='seq_fcn', # this is a placeholder and will be replaced by seq_fcn       seq_fcn=function(x){return(x[,1])}     ) M3 = model_apply(M3, predicted(M2))  nc = ncol(predicted(M2)) - ncol(predicted(M3)) cat(paste0('Number of features removed: ', nc)) ## Number of features removed: 262 M4 = wilcox_test(       alpha=1e-14,       factor_names='Type',        mtc='none',        predicted = 'significant'     ) +     filter_by_name(       mode='exclude',       dimension='variable',       seq_in='names',        names='place_holder',       seq_fcn=function(x){return(x$significant)}     ) M4 = model_apply(M4, predicted(M3))  nc = ncol(predicted(M3)) - ncol(predicted(M4)) cat(paste0('Number of features removed: ', nc)) ## Number of features removed: 169 M5 = rsd_filter(      rsd_threshold=20,      factor_name='Type' ) M5 = model_apply(M5,predicted(M4))  nc = ncol(predicted(M4)) - ncol(predicted(M5)) cat(paste0('Number of features removed: ', nc)) ## Number of features removed: 53"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"normalisation-missing-value-imputation-and-scaling","dir":"Articles","previous_headings":"A typical workflow for processing and analysing mass spectrometry-based metabolomics data.","what":"Normalisation, missing value imputation and scaling","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"apply number common pre-processing steps filtered peak matrix identical steps applied described Kirwan et al. 2013, 2014. Probabilistic Quotient Normalisation (PQN) k-nearest neighbours imputation (k = 5) Generalised log transform (glog) steps prepare data multivariate analysis accounting sample concentration differences, imputing missing values scaling data.","code":"# peak matrix processing M6 = pqn_norm(qc_label='QC',factor_name='Type') +       knn_impute(neighbours=5) +      glog_transform(qc_label='QC',factor_name='Type') M6 = model_apply(M6,predicted(M5))"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"exploratory-analysis","dir":"Articles","previous_headings":"A typical workflow for processing and analysing mass spectrometry-based metabolomics data.","what":"Exploratory Analysis","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"Principal Component Analysis (PCA) can used visualise high-dimensional data. unsupervised method maximises variance reduced number latent variables, principal components.  plot similar Figure 3b original publication link. Sample replicates represented colours samples groups (C = cow S = Sheep) different shapes. Plotting scores colouring Batch indicates signal/batch correction effective batches overlapping.","code":"# PCA M7  = mean_centre() + PCA(number_components = 2)  # apply model sequence to data M7 = model_apply(M7,predicted(M6))  # plot pca scores C = pca_scores_plot(factor_name=c('Sample_Rep','Class'),ellipse='none') chart_plot(C,M7[2]) + coord_fixed() +guides(colour=FALSE) ## Warning: The `<scale>` argument of `guides()` cannot be `FALSE`. Use \"none\" instead as ## of ggplot2 3.3.4. ## This warning is displayed once every 8 hours. ## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was ## generated. # chart object C = pca_scores_plot(factor_name=c('Batch'),ellipse='none') # plot chart_plot(C,M7[2]) + coord_fixed()"},{"path":[]},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"introduction-3","dir":"Articles","previous_headings":"Partial Least Squares (PLS) analysis of a untargeted LC-MS-based clinical metabolomics dataset.","what":"Introduction","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"aim vignette demonstrate 1) apply validate Partial Least Squares (PLS) analysis using structToolbox, 2) reproduce statistical analysis Thevenot et al. (2015) 3. compare different implementations PLS.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"dataset-2","dir":"Articles","previous_headings":"Partial Least Squares (PLS) analysis of a untargeted LC-MS-based clinical metabolomics dataset.","what":"Dataset","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"objective original study : > …study influence age, body mass index (bmi), gender metabolite concentrations urine, analysing 183 samples cohort adults liquid chromatography coupled high-resolution mass spectrometry. Thevenot et al. (2015) “Sacurine” dataset needs converted DatasetExperiment object. ropls package provides data list containing dataMatrix, sampleMetadata variableMetadata.","code":"data('sacurine',package = 'ropls') # the 'sacurine' list should now be available  # move the annotations to a new column and rename the features by index to avoid issues # later when data.frames get transposed and names get checked/changed sacurine$variableMetadata$annotation=rownames(sacurine$variableMetadata) rownames(sacurine$variableMetadata)=1:nrow(sacurine$variableMetadata) colnames(sacurine$dataMatrix)=1:ncol(sacurine$dataMatrix)  # create DatasetExperiment DE = DatasetExperiment(data = data.frame(sacurine$dataMatrix),                        sample_meta = sacurine$sampleMetadata,                        variable_meta = sacurine$variableMetadata,                        name = 'Sacurine data',                        description = 'See ropls package documentation for details')  # print summary DE ## A \"DatasetExperiment\" object ## ---------------------------- ## name:          Sacurine data ## description:   See ropls package documentation for details ## data:          183 rows x 109 columns ## sample_meta:   183 rows x 3 columns ## variable_meta: 109 rows x 4 columns"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"data-preprocessing","dir":"Articles","previous_headings":"Partial Least Squares (PLS) analysis of a untargeted LC-MS-based clinical metabolomics dataset.","what":"Data preprocessing","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"Sacurine dataset used within vignette already pre-processed: signal drift batch effect correction intensities, urine profile normalized osmolality sample. Finally, data log10 transformed.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"exploratory-data-analysis","dir":"Articles","previous_headings":"Partial Least Squares (PLS) analysis of a untargeted LC-MS-based clinical metabolomics dataset.","what":"Exploratory data analysis","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"Since data already processed data can visualised using Principal Component Analysis (PCA) without pre-processing. ropls package automatically applies unit variance scaling (autoscaling) default. approach applied .  third plot coloured gender (C) identical Figure 2 ropls package vignette. structToolbox package provides range PCA-related diagnostic plots, including D-statistic, scree, loadings plots. plots can used explore variance data.","code":"# prepare model sequence M = autoscale() + PCA(number_components = 5) # apply model sequence to dataset M = model_apply(M,DE)  # pca scores plots g=list() for (k in colnames(DE$sample_meta)) {     C = pca_scores_plot(factor_name = k)     g[[k]] = chart_plot(C,M[2]) } # plot using cowplot plot_grid(plotlist=g, nrow=1, align='vh', labels=c('A','B','C')) C = pca_scree_plot() g1 = chart_plot(C,M[2])  C = pca_loadings_plot() g2 = chart_plot(C,M[2])  C = pca_dstat_plot(alpha=0.95) g3 = chart_plot(C,M[2])  p1=plot_grid(plotlist = list(g1,g2),align='h',nrow=1,axis='b') p2=plot_grid(plotlist = list(g3),nrow=1) plot_grid(p1,p2,nrow=2)"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"partial-least-squares-pls-analysis","dir":"Articles","previous_headings":"Partial Least Squares (PLS) analysis of a untargeted LC-MS-based clinical metabolomics dataset.","what":"Partial Least Squares (PLS) analysis","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"ropls package uses implementation (O)PLS algorithms. structToolbox uses pls package, interesting compare outputs approaches. simplicity scores plots compared.  plot similar fig.3 ropls vignette. Differences due inverted LV axes, common occurrence NIPALS algorithm (used structToolbox ropls) depends algorithm initialised. compare R2 values model structToolbox use regression model, instead discriminant model. convert gender factor numeric variable applying model.  ropls package automatically applies cross-validation asses performance PLSDA model. structToolbox applied separately give control approach used desired. default cross-validation used ropls package 7-fold cross-validation replicate . Training set R2: 0.6975706 0.6798415 0.646671 0.6532914 0.7109769 0.670777 0.6935344 Test set Q2: 0.5460723 validity model can assessed using permutation testing. return discriminant model.  permuted models balanced accuracy around 50%, expected dataset two groups. unpermuted models balanced accuracy around 90% therefore much better might expected occur chance.","code":"# prepare model sequence M = autoscale() + PLSDA(factor_name='gender') M = model_apply(M,DE)  C = pls_scores_plot(factor_name = 'gender') chart_plot(C,M[2]) # convert gender to numeric DE$sample_meta$gender=as.numeric(DE$sample_meta$gender)  # models sequence M = autoscale(mode='both') + PLSR(factor_name='gender',number_components=3) M = model_apply(M,DE)  # some diagnostic charts C = plsr_cook_dist() g1 = chart_plot(C,M[2])  C = plsr_prediction_plot() g2 = chart_plot(C,M[2])  C = plsr_qq_plot() g3 = chart_plot(C,M[2])  C = plsr_residual_hist() g4 = chart_plot(C,M[2])  plot_grid(plotlist = list(g1,g2,g3,g4), nrow=2,align='vh') # model sequence M = kfold_xval(folds=7, factor_name='gender') *      (autoscale(mode='both') + PLSR(factor_name='gender')) M = run(M,DE,r_squared()) # reset gender to original factor DE$sample_meta$gender=sacurine$sampleMetadata$gender # model sequence M = permutation_test(number_of_permutations = 10, factor_name='gender') *     kfold_xval(folds=7,factor_name='gender') *     (autoscale() + PLSDA(factor_name='gender',number_components = 3)) M = run(M,DE,balanced_accuracy())  C = permutation_test_plot(style='boxplot') chart_plot(C,M)+ylab('1 - balanced accuracy')"},{"path":[]},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"introduction-4","dir":"Articles","previous_headings":"Univariate and multivariate statistical analysis of a NMR-based clinical metabolomics dataset.","what":"Introduction","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"purpose vignette demonstrate different functionalities methods available part structToolbox reproduce data analysis reported Mendez et al., (2020) Chan et al., (2016).","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"dataset-3","dir":"Articles","previous_headings":"Univariate and multivariate statistical analysis of a NMR-based clinical metabolomics dataset.","what":"Dataset","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"1H-NMR dataset used described Mendez et al., (2020) vignette contains processed spectra urine samples obtained gastric cancer healthy patients Chan et al., (2016). experimental raw data available Metabolomics Workbench (PR000699) processed version available Excel data file. first step need reorganise convert Excel data file DatasetExperiment object. Using openxlsx package file can read directly R data.frame manipulated required.","code":"url = 'https://github.com/CIMCB/MetabWorkflowTutorial/raw/master/GastricCancer_NMR.xlsx'  # read in file directly from github... # X=read.xlsx(url)  # ...or use BiocFileCache path = bfcrpath(bfc,url) X = read.xlsx(path)  # sample meta data SM=X[,1:4] rownames(SM)=SM$SampleID # convert to factors SM$SampleType=factor(SM$SampleType) SM$Class=factor(SM$Class) # keep a numeric version of class for regression SM$Class_num = as.numeric(SM$Class)  ## data matrix # remove meta data X[,1:4]=NULL rownames(X)=SM$SampleID  # feature meta data VM=data.frame(idx=1:ncol(X)) rownames(VM)=colnames(X)  # prepare DatasetExperiment DE = DatasetExperiment(     data=X,     sample_meta=SM,     variable_meta=VM,     description='1H-NMR urinary metabolomic profiling for diagnosis of gastric cancer',     name='Gastric cancer (NMR)')  DE ## A \"DatasetExperiment\" object ## ---------------------------- ## name:          Gastric cancer (NMR) ## description:   1H-NMR urinary metabolomic profiling for diagnosis of gastric cancer ## data:          140 rows x 149 columns ## sample_meta:   140 rows x 5 columns ## variable_meta: 149 rows x 1 columns"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"data-pre-processing-and-quality-assessment","dir":"Articles","previous_headings":"Univariate and multivariate statistical analysis of a NMR-based clinical metabolomics dataset.","what":"Data pre-processing and quality assessment","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"good practice remove features may low quality, assess quality data general. Tutorial features QC-RSD > 20% 10% features missing retained. Note additional feature vs processing reported Mendez et. al. filters use >= <= instead > <. suitable scaling transformation PCA can used assess data quality. expected biological variance (samples) larger technical variance (QCs). workflow reproducing (link) following steps applied: log10 transform autoscaling (scaled unit variance) knn imputation (3 neighbours) transformed scaled matrix used input PCA. Using struct can chain steps single model sequence.","code":"# prepare model sequence M = rsd_filter(rsd_threshold=20,qc_label='QC',factor_name='Class') +     mv_feature_filter(threshold = 10,method='across',factor_name='Class')  # apply model M = model_apply(M,DE)  # get the model output filtered = predicted(M)  # summary of filtered data filtered ## A \"DatasetExperiment\" object ## ---------------------------- ## name:          Gastric cancer (NMR) ## description:   1H-NMR urinary metabolomic profiling for diagnosis of gastric cancer ## data:          140 rows x 53 columns ## sample_meta:   140 rows x 5 columns ## variable_meta: 53 rows x 1 columns # prepare the model sequence M = log_transform(base = 10) +     autoscale() +      knn_impute(neighbours = 3) +     PCA(number_components = 10)  # apply model sequence to data M = model_apply(M,filtered)  # get the transformed, scaled and imputed matrix TSI = predicted(M[3])  # scores plot C = pca_scores_plot(factor_name = 'SampleType') g1 = chart_plot(C,M[4])  # loadings plot C = pca_loadings_plot() g2 = chart_plot(C,M[4])  plot_grid(g1,g2,align='hv',nrow=1,axis='tblr')"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"univariate-statistics","dir":"Articles","previous_headings":"Univariate and multivariate statistical analysis of a NMR-based clinical metabolomics dataset.","what":"Univariate statistics","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"structToolbox provides number objects ttest, counting numbers features etc. brevity ttest calculated comparison workflow following (link). QC samples need excluded, data reduced GC groups.","code":"# prepare model TT = filter_smeta(mode='include',factor_name='Class',levels=c('GC','HE')) +        ttest(alpha=0.05,mtc='fdr',factor_names='Class')  # apply model TT = model_apply(TT,filtered)  # keep the data filtered by group for later filtered = predicted(TT[1])  # convert to data frame out=as_data_frame(TT[2])  # show first few features head(out) ##     t_statistic   t_p_value t_significant estimate.mean.GC estimate.mean.HE ## M4    3.5392652 0.008421042          TRUE         51.73947         26.47778 ## M5   -1.4296604 0.410396437         FALSE        169.91500        265.11860 ## M7   -2.7456506 0.051494976         FALSE         53.98718        118.52558 ## M8    2.1294198 0.178392032         FALSE         79.26750         54.39535 ## M11  -0.5106536 0.776939682         FALSE        171.27949        201.34390 ## M14   1.4786810 0.403091881         FALSE         83.90250         61.53171 ##           lower     upper ## M4    10.961769  39.56162 ## M5  -228.454679  38.04747 ## M7  -111.468619 -17.60818 ## M8     1.543611  48.20069 ## M11 -147.434869  87.30604 ## M14   -7.835950  52.57754"},{"path":[]},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"training-and-test-sets","dir":"Articles","previous_headings":"Univariate and multivariate statistical analysis of a NMR-based clinical metabolomics dataset. > Multivariate statistics and machine learning","what":"Training and Test sets","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"Splitting data training test sets important aspect machine learning. structToolbox implemented using split_data object random subsampling across whole dataset, stratified_split splitting based group sizes, approach used Mendez et al.","code":"# prepare model M = stratified_split(p_train=0.75,factor_name='Class') # apply to filtered data M = model_apply(M,filtered) # get data from object train = M$training train ## A \"DatasetExperiment\" object ## ---------------------------- ## name:          Gastric cancer (NMR) ##                (Training set) ## description:   • 1H-NMR urinary metabolomic profiling for diagnosis of gastric cancer ##                • A subset of the data has been selected as a training set ## data:          62 rows x 53 columns ## sample_meta:   62 rows x 5 columns ## variable_meta: 53 rows x 1 columns cat('\\n') test = M$testing test ## A \"DatasetExperiment\" object ## ---------------------------- ## name:          Gastric cancer (NMR) ##                (Testing set) ## description:   • 1H-NMR urinary metabolomic profiling for diagnosis of gastric cancer ##                • A subset of the data has been selected as a test set ## data:          21 rows x 53 columns ## sample_meta:   21 rows x 5 columns ## variable_meta: 53 rows x 1 columns"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"optimal-number-of-pls-components","dir":"Articles","previous_headings":"Univariate and multivariate statistical analysis of a NMR-based clinical metabolomics dataset. > Multivariate statistics and machine learning","what":"Optimal number of PLS components","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"Mendez et al k-fold cross-validation used determine optimal number PLS components. 100 bootstrap iterations used generate confidence intervals. strucToolbox implemented using “iterator” objects, can combined model objects. R2 used metric optimisation, PLSR model structToolbox used. speed 10 bootstrap iterations used .  chart plotted shows Q2, comparable Figure 13 Mendez et al . Two components selected Mendez et al, use .","code":"# scale/transform training data M = log_transform(base = 10) +     autoscale() +      knn_impute(neighbours = 3,by='samples')  # apply model M = model_apply(M,train)  # get scaled/transformed training data train_st = predicted(M)  # prepare model sequence MS = grid_search_1d(         param_to_optimise = 'number_components',         search_values = as.numeric(c(1:6)),         model_index = 2,         factor_name = 'Class_num',         max_min = 'max') *      permute_sample_order(         number_of_permutations = 10) *      kfold_xval(         folds = 5,         factor_name = 'Class_num') *       (mean_centre(mode='sample_meta')+       PLSR(factor_name='Class_num'))  # run the validation MS = struct::run(MS,train_st,r_squared())  # C = gs_line() chart_plot(C,MS)"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"pls-model-evalutation","dir":"Articles","previous_headings":"Univariate and multivariate statistical analysis of a NMR-based clinical metabolomics dataset. > Multivariate statistics and machine learning","what":"PLS model evalutation","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"evaluate model discriminant analysis structToolbox PLSDA model appropriate.  Note default cutoff B figure PLS models structToolbox 0, groups encoded +/-1. impact overall performance model.","code":"# prepare the discriminant model P = PLSDA(number_components = 2, factor_name='Class')  # apply the model P = model_apply(P,train_st)  # charts C = plsda_predicted_plot(factor_name='Class',style='boxplot') g1 = chart_plot(C,P)  C = plsda_predicted_plot(factor_name='Class',style='density') g2 = chart_plot(C,P)+xlim(c(-2,2))  C = plsda_roc_plot(factor_name='Class') g3 = chart_plot(C,P)  plot_grid(g1,g2,g3,align='vh',axis='tblr',nrow=1, labels=c('A','B','C')) # AUC for comparison with Mendez et al MET = calculate(AUC(),P$y$Class,P$yhat[,1]) MET ## A \"AUC\" object ## -------------- ## name:          Area under ROC curve ## description:   The area under the ROC curve of a classifier is estimated using the trapezoid method. ## value:         0.9739583"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"permutation-test","dir":"Articles","previous_headings":"Univariate and multivariate statistical analysis of a NMR-based clinical metabolomics dataset. > Multivariate statistics and machine learning","what":"Permutation test","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"permutation test can used assess likely observed result occurred chance. structToolbox permutation_test iterator object can combined iterators models.  plot comparable bottom half Figure 17 Mendez et. al.. unpermuted (true) Q2 values consistently better permuted (null) models. .e. model reliable.","code":"# model sequence MS = permutation_test(number_of_permutations = 20,factor_name = 'Class_num') *       kfold_xval(folds = 5,factor_name = 'Class_num') *      (mean_centre(mode='sample_meta') + PLSR(factor_name='Class_num', number_components = 2))  # run iterator MS = struct::run(MS,train_st,r_squared())  # chart C = permutation_test_plot(style = 'density')  chart_plot(C,MS) + xlim(c(-1,1)) + xlab('R Squared')"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"pls-projection-plots","dir":"Articles","previous_headings":"Univariate and multivariate statistical analysis of a NMR-based clinical metabolomics dataset. > Multivariate statistics and machine learning","what":"PLS projection plots","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"PLS can also used visualise model interpret latent variables.","code":"# prepare the discriminant model P = PLSDA(number_components = 2, factor_name='Class')  # apply the model P = model_apply(P,train_st)  C = pls_scores_plot(components=c(1,2),factor_name = 'Class') chart_plot(C,P)"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"pls-feature-importance","dir":"Articles","previous_headings":"Univariate and multivariate statistical analysis of a NMR-based clinical metabolomics dataset. > Multivariate statistics and machine learning","what":"PLS feature importance","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"Regression coefficients VIP scores can used estimate importance individual features PLS model. Mendez et al bootstrapping used estimate confidence intervals, brevity skip .","code":"# prepare chart C = pls_vip_plot(ycol = 'HE') g1 = chart_plot(C,P)  C = pls_regcoeff_plot(ycol='HE') g2 = chart_plot(C,P)  plot_grid(g1,g2,align='hv',axis='tblr',nrow=2)"},{"path":[]},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"introduction-5","dir":"Articles","previous_headings":"Classification of Metabolomics Data using Support Vector Machines.","what":"Introduction","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"aim vignette illustrate apply SVM analysis Classifying Metabolomics data. Support vector Machines (SVM) commonly used method Machine Learning. classification tasks used generate boundary groups samples training set. well generating linear boundaries, SVM can extended exploit use kernels generate complex non-linear boundaries groups required. structToolbox package, SVM functionality provided e1071 package incorporated model object. chart object (svm_plot_2d) also available plot SVM boundaries data two variables.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"dataset-4","dir":"Articles","previous_headings":"Classification of Metabolomics Data using Support Vector Machines.","what":"Dataset","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"1H-NMR dataset used described Mendez et al., (2020) vignette contains processed spectra urine samples obtained gastric cancer healthy patients Chan et al., (2016). raw experimental data available Metabolomics Workbench (PR000699) processed version available Excel data file. simplicity use pre-processed version 1H-NMR “Gastric cancer” dataset using structToolbox package. Details regards pre-processing reported “NMR_clinical_metabolomics” vignette `r Biocpkg(“structToolbox”) package. purposes illustrating effect SVM parameters boundary groups, reduce data include GC groups apply PLS reduce data two components. treat PLS scores two group dataset two features.","code":"# summary of DatasetExperiment object DE ## A \"DatasetExperiment\" object ## ---------------------------- ## name:           ## description:    ## data:          140 rows x 53 columns ## sample_meta:   140 rows x 5 columns ## variable_meta: 53 rows x 1 columns # model sequence and pls model (NB data already centred) MS = filter_smeta(mode = 'include', levels = c('GC','HE'), factor_name = 'Class') +      PLSDA(factor_name = 'Class',number_components = 2)  # apply PLS model MS = model_apply(MS,DE)  # plot the data C = pls_scores_plot(factor_name = 'Class') chart_plot(C,MS[2]) # new DatasetExperiment object from the PLS scores DE2 = DatasetExperiment(   data = MS[2]$scores$data,    sample_meta = predicted(MS[1])$sample_meta,   variable_meta = data.frame('LV'=c(1,2),row.names = colnames(MS[2]$scores)),   name = 'Illustrativate SVM dataset',   description = 'Generated by applying PLS to the processed Gastric cancer (NMR) dataset' )  DE2 ## A \"DatasetExperiment\" object ## ---------------------------- ## name:          Illustrativate SVM dataset ## description:   Generated by applying PLS to the processed Gastric cancer (NMR) dataset ## data:          83 rows x 2 columns ## sample_meta:   83 rows x 5 columns ## variable_meta: 2 rows x 1 columns"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"basic-svm-model","dir":"Articles","previous_headings":"Classification of Metabolomics Data using Support Vector Machines.","what":"Basic SVM model","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"simplest SVM model uses linear kernel. structToolbox SVM model can used train apply SVM models. svm_plot_2d chart object provided visualisation boundaries two dimensions.  SVM boundary plotted black, margins grey support vectors indicated grey circles.","code":"# SVM model M = SVM(   factor_name = 'Class',   kernel = 'linear' )  # apply model M = model_apply(M,DE2)  # plot boundary C = svm_plot_2d(factor_name = 'Class') chart_plot(C,M, DE2)"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"svm-cost-function","dir":"Articles","previous_headings":"Classification of Metabolomics Data using Support Vector Machines.","what":"SVM cost function","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"SVM cost function applies penalty samples wrong side margins. high penalty results narrow margin tries force samples correct side boundary. low penalty makes wider margin less strict samples misclassified. optimal cost use data dependent.","code":"# low cost M$cost=0.01 M=model_apply(M,DE2) C=svm_plot_2d(factor_name='Species') g1=chart_plot(C,M,DE2)  # medium cost M$cost=0.05 M=model_apply(M,DE2) C=svm_plot_2d(factor_name='Species') g2=chart_plot(C,M,DE2)  # high cost M$cost=100 M=model_apply(M,DE2)  C=svm_plot_2d(factor_name='Species') g3=chart_plot(C,M,DE2)  # plot prow <- plot_grid(   g1 + theme(legend.position=\"none\"),   g2 + theme(legend.position=\"none\"),   g3 + theme(legend.position=\"none\"),   align = 'vh',   labels = c(\"Low cost\", \"Medium cost\", \"High cost\"),   hjust = -1,   nrow = 2 )  legend <- get_legend(   # create some space to the left of the legend   g1 + guides(color = guide_legend(nrow = 1)) +   theme(legend.position = \"bottom\") ) ## Warning in get_plot_component(plot, \"guide-box\"): Multiple components found; ## returning the first one. To return all, use `return_all = TRUE`. plot_grid(prow, legend, ncol=1, rel_heights = c(1, .1))"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"kernel-functions","dir":"Articles","previous_headings":"Classification of Metabolomics Data using Support Vector Machines.","what":"Kernel functions","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"number different kernels can used support vector machines. structToolbox wrapper ‘linear’, ‘polynomial’,‘radial’ ‘sigmoid’ kernels can specified. Using kernels allows boundary flexible, often require additional parameters specified. best kernel use vary depending dataset, common choice radial kernel allows high flexibility single parameter.  parameters kernel can used control complexity boundary. show radial kernel parameter “gamma” can used change complexity boundary. combination cost parameter (keep constant ) allows highly flexible boundary models.  Note best practice select optimal kernel parameter(s) combination cost parameter (e.g. 2d grid search) best combination identified.","code":"# set a fixed cost for this comparison M$cost=1  # linear kernel M$kernel='linear' M=model_apply(M,DE2) C=svm_plot_2d(factor_name='Species') g1=chart_plot(C,M,DE2)  # polynomial kernel M$kernel='polynomial' M$gamma=1 M$coef0=0 M=model_apply(M,DE2) C=svm_plot_2d(factor_name='Species') g2=chart_plot(C,M,DE2)  # rbf kernel M$kernel='radial' M$gamma=1 M=model_apply(M,DE2) C=svm_plot_2d(factor_name='Species') g3=chart_plot(C,M,DE2)  # sigmoid kernel M$kernel='sigmoid' M$gamma=1 M$coef0=0 M=model_apply(M,DE2) C=svm_plot_2d(factor_name='Species') g4=chart_plot(C,M,DE2)  # plot prow <- plot_grid(   g1 + theme(legend.position=\"none\"),   g2 + theme(legend.position=\"none\"),   g3 + theme(legend.position=\"none\"),   g4 + theme(legend.position=\"none\"),   align = 'vh',   labels = c(\"Linear\", \"Polynomial\", \"Radial\",\"Sigmoid\"),   hjust = -1,   nrow = 2 ) legend <- get_legend(   # create some space to the left of the legend   g1 + guides(color = guide_legend(nrow = 1)) +     theme(legend.position = \"bottom\") ) ## Warning in get_plot_component(plot, \"guide-box\"): Multiple components found; ## returning the first one. To return all, use `return_all = TRUE`. plot_grid(prow, legend, ncol = 1, rel_heights = c(1, .1)) # rbf kernel and cost M$kernel = 'radial' M$cost = 1  # low gamma M$gamma=0.01 M=model_apply(M,DE2) C=svm_plot_2d(factor_name='Species') g1=chart_plot(C,M,DE2)  # medium gamma M$gamma=0.1 M=model_apply(M,DE2) C=svm_plot_2d(factor_name='Species') g2=chart_plot(C,M,DE2)  # high gamma M$gamma=1 M=model_apply(M,DE2) C=svm_plot_2d(factor_name='Species') g3=chart_plot(C,M,DE2)  # plot prow <- plot_grid(  g1 + theme(legend.position=\"none\"),  g2 + theme(legend.position=\"none\"),  g3 + theme(legend.position=\"none\"),  align = 'vh',  labels = c(\"Low gamma\", \"Medium gamma\", \"High gamma\"),  hjust = -1,  nrow = 2 ) legend <- get_legend(   # create some space to the left of the legend   g1 + guides(color = guide_legend(nrow = 1)) +   theme(legend.position = \"bottom\") ) ## Warning in get_plot_component(plot, \"guide-box\"): Multiple components found; ## returning the first one. To return all, use `return_all = TRUE`. plot_grid(prow, legend, ncol = 1, rel_heights = c(1, .1))"},{"path":[]},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"introduction-6","dir":"Articles","previous_headings":"Exploratory data analysis of LC-MS-based proteomics and metabolomics datasets (STATegra project)","what":"Introduction","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"aim vignette conduct data preprocessing exploratory analysis data STATegra project (https://www.nature.com/articles/s41597-019-0202-7). demonstration purposes focus Proteomics Metabolomics datasets publicly available part STATegra multi-omics dataset. …STATegra multi-omics dataset combines measurements 10 different omics technologies applied biological system, namely well-studied mouse pre-B-cell differentiation. STATegra includes high-throughput measurements chromatin structure, gene expression, proteomics metabolomics, complemented single-cell data. Gomez-Cabrero et al STATegra includes high-throughput measurements chromatin structure, gene expression, proteomics metabolomics, complemented single-cell data.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"lc-ms-based-proteomics-dataset","dir":"Articles","previous_headings":"Exploratory data analysis of LC-MS-based proteomics and metabolomics datasets (STATegra project)","what":"LC-MS-based proteomics dataset","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"LC-MS-based proteomics dataset STATegra multi-omics dataset (see Introduction) can found github must extracted zip file prior data analysis. imported data needs converted DatasetExperiment format use structToolbox. number Reporter genes included study. plot two illustrate trends data.","code":"# path to zip zipfile = \"https://raw.github.com/STATegraData/STATegraData/master/Script_STATegra_Proteomics.zip\"  ## retrieve from BiocFileCache path = bfcrpath(bfc,zipfile) temp = bfccache(bfc)  ## ... or download to temp location # path = tempfile() # temp = tempdir() # download.file(zipfile,path)  # unzip unzip(path, files = \"Proteomics_01_uniprot_canonical_normalized.txt\", exdir=temp) # read samples all_data <-  read.delim(file.path(temp,\"Proteomics_01_uniprot_canonical_normalized.txt\"), as.is = TRUE, header = TRUE, sep = \"\\t\") # extract data matrix data = all_data[1:2527,51:86] # shorten sample names colnames(data) = lapply(colnames(data), function (x) substr(x, 27, nchar(x))) # replace 0 with NA data[data == 0] <- NA # transpose data=as.data.frame(t(data))  # prepare sample meta SM = lapply(rownames(data),function(x) {   s=strsplit(x,'_')[[1]] # split at underscore   out=data.frame(     'treatment' = s[[1]],     'time' = substr(s[[2]],1,nchar(s[[2]])-1) ,         'batch' = substr(s[[3]],6,nchar(s[[3]])),     'condition' = substr(x,1,6) # interaction between treatment and time   )   return(out) }) SM = do.call(rbind,SM) rownames(SM)=rownames(data) # convert to factors SM$treatment=factor(SM$treatment) SM$time=ordered(SM$time,c(\"0\",\"2\",\"6\",\"12\",\"18\",\"24\")) SM$batch=ordered(SM$batch,c(1,3,4,5,6,7)) SM$condition=factor(SM$condition)  # variable meta data VM = all_data[1:2527,c(1,6,7)] rownames(VM)=colnames(data)  # prepare DatasetExperiment DS = DatasetExperiment(   data = data,    sample_meta = SM,    variable_meta = VM,    name = 'STATegra Proteomics',   description = 'downloaded from: https://github.com/STATegraData/STATegraData/' ) DS ## A \"DatasetExperiment\" object ## ---------------------------- ## name:          STATegra Proteomics ## description:   downloaded from: https://github.com/STATegraData/STATegraData/ ## data:          36 rows x 2527 columns ## sample_meta:   36 rows x 4 columns ## variable_meta: 2527 rows x 3 columns # find id of reporters Ldha = which(DS$variable_meta$Gene.names=='Ldha') Hk2 = which(DS$variable_meta$Gene.names=='Hk2')  # chart object C = feature_boxplot(feature_to_plot=Ldha,factor_name='time',label_outliers=FALSE) g1=chart_plot(C,DS)+ggtitle('Ldha')+ylab('expression') C = feature_boxplot(feature_to_plot=Hk2,factor_name='time',label_outliers=FALSE) g2=chart_plot(C,DS)+ggtitle('Hk2')+ylab('expression')  plot_grid(g1,g2,nrow=1,align='vh',axis='tblr')"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"data-transformation","dir":"Articles","previous_headings":"Exploratory data analysis of LC-MS-based proteomics and metabolomics datasets (STATegra project) > LC-MS-based proteomics dataset","what":"Data transformation","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"data log2 transformed, scaled mean medians equal conditions. steps available structToolbox using log_transform mean_of_medians objects. Reporter genes plotted comparison.","code":"# prepare model sequence M = log_transform(   base=2) +    mean_of_medians(     factor_name = 'condition') # apply model sequence M = model_apply(M,DS)   # get transformed data DST = predicted(M) # chart object C = feature_boxplot(feature_to_plot=Ldha,factor_name='time',label_outliers=FALSE) g1=chart_plot(C,DST)+ggtitle('Ldha')+ylab('log2(expression)') C = feature_boxplot(feature_to_plot=Hk2,factor_name='time',label_outliers=FALSE) g2=chart_plot(C,DST)+ggtitle('Hk2')+ylab('log2(expression)')  plot_grid(g1,g2,nrow=1,align='vh',axis='tblr')"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"missing-value-filtering","dir":"Articles","previous_headings":"Exploratory data analysis of LC-MS-based proteomics and metabolomics datasets (STATegra project) > LC-MS-based proteomics dataset","what":"Missing value filtering","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"Missing value filtering involves removing feature (gene) least 3 missing values per group least 11 samples. specific filter structToolbox time, can achieved combining filter_na_count filter_by_name objects. Specifically, default output filter_na_count changed return matrix NA counts per class. output connected ‘names’ input filter_by_names converted TRUE/FALSE using ‘seq_fcn’ input. ‘seq_fcn’ function processes NA counts used inputs filter_by_names. data passed along model sequence passes unchanged filter_na_count object becuase default output changed, filter_na_count filter_by_name objects working together single filter.","code":"# build model sequence M2 = filter_na_count(   threshold=2,   factor_name='condition',    predicted='na_count') +    # override the default output   filter_by_name(     mode='exclude',     dimension='variable',     names='place_holder',     seq_in='names',     seq_fcn=function(x) {      # convert NA count pre group to true/false       x=x>2                  # more the two missing per group       x=rowSums(x)>10        # in more than 10 groups       return(x)     }   ) # apply to transformed data M2 = model_apply(M2,DST)  # get the filtered data DSTF = predicted(M2)"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"missing-value-imputation","dir":"Articles","previous_headings":"Exploratory data analysis of LC-MS-based proteomics and metabolomics datasets (STATegra project) > LC-MS-based proteomics dataset","what":"Missing value imputation","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"STATegra uses two imputation methods available struct objects, create temporary STATegra_impute objects using functions struct package. first imputation method imputes missing values treatment values missing samples using “random value discovery”. create new struct object using set_struct_obj global environment, “method_apply” method implements imputation. second imputation method replacing missing values condition exactly 1 missing value mean values condition. create new struct object corresponding method new object implement filter. new STATegra imputation objects can now used model sequences like struct object. final filter added remove feature missing values imputation.","code":"# create new imputation object set_struct_obj(   class_name = 'STATegra_impute1',   struct_obj = 'model',   params=c(factor_sd='character',factor_name='character'),   outputs=c(imputed='DatasetExperiment'),   prototype = list(     name = 'STATegra imputation 1',     description = 'If missing values are present for all one group then they are replaced with min/2 + \"random value below discovery\".',     predicted = 'imputed'   )  )  # create method_apply for imputation method 1 set_obj_method(   class_name='STATegra_impute1',   method_name='model_apply',   definition=function(M,D) {          # for each feature count NA within each level     na = apply(D$data,2,function(x){       tapply(x,D$sample_meta[[M$factor_name]],function(y){         sum(is.na(y))       })     })     # count number of samples in each group     count=summary(D$sample_meta[[M$factor_name]])     # standard deviation of features within levels of factor_sd     sd = apply(D$data,2,function(x) {tapply(x,D$sample_meta[[M$factor_sd]],sd,na.rm=TRUE)})     sd = median(sd,na.rm=TRUE)          # impute or not     check=na == matrix(count,nrow=2,ncol=ncol(D)) # all missing in one class          # impute matrix     mi = D$data     for (j in 1:nrow(mi)) {       # index of group for this sample       g = which(levels(D$sample_meta[[M$factor_name]])==D$sample_meta[[M$factor_name]][j])       iv=rnorm(ncol(D),min(D$data[j,],na.rm=TRUE)/2,sd)       mi[j,is.na(mi[j,]) & check[g,]] = iv[is.na(mi[j,]) & check[g,]]     }     D$data = mi     M$imputed=D     return(M)   } ) # create new imputation object set_struct_obj(   class_name = 'STATegra_impute2',   struct_obj = 'model',   params=c(factor_name='character'),   outputs=c(imputed='DatasetExperiment'),   prototype = list(     name = 'STATegra imputation 2',     description = 'For those conditions with only 1 NA impute with the mean of the condition.',     predicted = 'imputed'   )  )  # create method_apply for imputation method 2 set_obj_method(   class_name='STATegra_impute2',   method_name='model_apply',   definition=function(M,D) {     # levels in condition     L = levels(D$sample_meta[[M$factor_name]])          # for each feature count NA within each level     na = apply(D$data,2,function(x){       tapply(x,D$sample_meta[[M$factor_name]],function(y){         sum(is.na(y))       })     })          # standard deviation of features within levels of factor_sd     sd = apply(D$data,2,function(x) {tapply(x,D$sample_meta[[M$factor_name]],sd,na.rm=TRUE)})     sd = median(sd,na.rm=TRUE)          # impute or not     check=na == 1 # only one missing for a condition          # index of samples for each condition     IDX = list()     for (k in L) {       IDX[[k]]=which(D$sample_meta[[M$factor_name]]==k)     }          ## impute     # for each feature     for (k in 1:ncol(D)) {       # for each condition       for (j in 1:length(L)) {         # if passes test         if (check[j,k]) {           # mean of samples in group           m = mean(D$data[IDX[[j]],k],na.rm=TRUE)           # imputed value           im = rnorm(1,m,sd)           # replace NA with imputed           D$data[is.na(D$data[,k]) & D$sample_meta[[M$factor_name]]==L[j],k]=im         }       }     }     M$imputed=D     return(M)   } ) # model sequence M3 = STATegra_impute1(factor_name='treatment',factor_sd='condition') +      STATegra_impute2(factor_name = 'condition') +       filter_na_count(threshold = 3, factor_name='condition') # apply model M3 = model_apply(M3,DSTF) # get imputed data DSTFI = predicted(M3) DSTFI ## A \"DatasetExperiment\" object ## ---------------------------- ## name:          STATegra Proteomics ## description:   downloaded from: https://github.com/STATegraData/STATegraData/ ## data:          36 rows x 864 columns ## sample_meta:   36 rows x 4 columns ## variable_meta: 864 rows x 3 columns"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"exploratory-analysis-1","dir":"Articles","previous_headings":"Exploratory data analysis of LC-MS-based proteomics and metabolomics datasets (STATegra project) > LC-MS-based proteomics dataset","what":"Exploratory analysis","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"often useful visualise distribution values across samples verify transformations/normalisation/filtering etc effective.  values longer skewed show approximately normal distribution. boxplots comparable width outliers indicated, transformations etc overall positive effect. PCA used provide graphical representation data. comparison outputs STATegra filter included reduce data include treated samples (IKA)  appear strong batch effect. PC1 dominated time point “24” potentially outlying points time points “2” “0”.","code":"# model sequence P = filter_smeta(mode='include',factor_name='treatment',levels='IKA') +     mean_centre() +      PCA(number_components = 2) # apply model P = model_apply(P,DSTFI)  # scores plots coloured by factors g = list() for (k in c('batch','time')) {   C = pca_scores_plot(factor_name=k,ellipse='none')   g[[k]]=chart_plot(C,P[3]) }  plot_grid(plotlist = g,nrow=1)"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"lc-ms-based-metabolomics-dataset","dir":"Articles","previous_headings":"Exploratory data analysis of LC-MS-based proteomics and metabolomics datasets (STATegra project)","what":"LC-MS-based metabolomics dataset","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"LC-MS-based metabolomics dataset STATegra multi-omics dataset (see Introduction) can found github must extracted zip file prior data analysis. imported data needs converted DatasetExperiment format use structToolbox.","code":"# path to zip zipfile = \"https://raw.github.com/STATegraData/STATegraData/master/Script_STATegra_Metabolomics.zip\"  ## retrieve from BiocFileCache path = bfcrpath(bfc,zipfile) temp = bfccache(bfc)  ## ... or download to temp location # path = tempfile() # temp = tempdir() # download.file(zipfile,path)  # unzip unzip(zipfile=path, files = \"LC_MS_raw_data.xlsx\", exdir=temp)  # read samples data <- as.data.frame(read.xlsx(file.path(temp,\"LC_MS_raw_data.xlsx\"),sheet = 'Data')) # extract sample meta data SM = data[ ,1:8]  # add coloumn for sample type (QC, blank etc) blanks=c(1,2,33,34,65,66) QCs=c(3,4,11,18,25,32,35,36,43,50,57,64) SM$sample_type='Sample' SM$sample_type[blanks]='Blank' SM$sample_type[QCs]='QC'  # put qc/blank labels in all factors for plotting later SM$biol.batch[SM$sample_type!='Sample']=SM$sample_type[SM$sample_type!='Sample'] SM$time.point[SM$sample_type!='Sample']=SM$sample_type[SM$sample_type!='Sample'] SM$condition[SM$sample_type!='Sample']=SM$sample_type[SM$sample_type!='Sample']  # convert to factors SM$biol.batch=ordered(SM$biol.batch,c('9','10','11','12','QC','Blank')) SM$time.point=ordered(SM$time.point,c('0h','2h','6h','12h','18h','24h','QC','Blank')) SM$condition=factor(SM$condition) SM$sample_type=factor(SM$sample_type)  # variable meta data VM = data.frame('annotation'=colnames(data)[9:ncol(data)])  # raw data X = data[,9:ncol(data)] # convert 0 to NA X[X==0]=NA # force to numeric; any non-numerics will become NA X=data.frame(lapply(X,as.numeric),check.names = FALSE)  # make sure row/col names match rownames(X)=data$label rownames(SM)=data$label rownames(VM)=colnames(X)   # create DatasetExperiment object DE = DatasetExperiment(   data = X,    sample_meta = SM,    variable_meta = VM,    name = 'STATegra Metabolomics LCMS',   description = 'https://www.nature.com/articles/s41597-019-0202-7' )  DE ## A \"DatasetExperiment\" object ## ---------------------------- ## name:          STATegra Metabolomics LCMS ## description:   https://www.nature.com/articles/s41597-019-0202-7 ## data:          66 rows x 152 columns ## sample_meta:   66 rows x 9 columns ## variable_meta: 152 rows x 1 columns"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"data-preprocessing-1","dir":"Articles","previous_headings":"Exploratory data analysis of LC-MS-based proteomics and metabolomics datasets (STATegra project) > LC-MS-based metabolomics dataset","what":"Data preprocessing","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"STATegra project LCMS data combined GCMS data multiblock analysis conducted. LCMS explored, data processed differently comparison Gomez-Cabrero et al. basic processing steps applied order generate valid PCA plot biological QC samples.","code":"# prepare model sequence MS = filter_smeta(mode = 'include', levels='QC', factor_name = 'sample_type') +      knn_impute(neighbours=5) +      vec_norm() +       log_transform(base = 10)   # apply model sequence MS = model_apply(MS, DE) ## Warning in knnimp(x, k, maxmiss = rowmax, maxp = maxp): 3 rows with more than 50 % entries missing; ##  mean imputation used for these rows"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"exploratory-analysis-2","dir":"Articles","previous_headings":"Exploratory data analysis of LC-MS-based proteomics and metabolomics datasets (STATegra project) > LC-MS-based metabolomics dataset","what":"Exploratory analysis","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"First use PCA look QC samples order make assessment data quality.  QC labelled “36” clearly different QCs. STATegra QC removed, exclude well. corresponds QC H1. STATegra also excluded QC samples measured immediately blank, also .  Now plot QC samples context samples. several possible approaches, apply approach applying PCA full dataset including QCs. exclude blanks likely dominate plot removed. samples batch 12 excluded STATegra replicate . QCs appear representative samples, strong clusters data, including QC samples biological variation. likely number ‘low quality’ features excluded, now, use sophisticated normalisation (PQN) scaling methods (glog).  can see now QCs tightly clustered. indicates biological variance remaining high quality features much greater technical variance represented QCs. appear trend measurement order (), important indicator instrument drift throughout run large source variation dataset. appear strong clustering related biological batch (B). appear strong trend time (C) likely subtle variation might masked sources variance stage. clustering related condition (D) overlap. explore trends time, split data condition factor explore Ikaros group. Removing condition factor variation potentially make easier spot subtle trends. extract glog transformed matrix previous model sequence continue .  Colouring groups () makes time point trend difficult see, adding ggplot continuous colour scale “viridis” (B) trend time along PC1 becomes much clearer.","code":"# pca model sequence M =  mean_centre() +      PCA(number_components = 3)  # apply model M = model_apply(M,predicted(MS))   # PCA scores plot C = pca_scores_plot(factor_name = 'sample_type',label_factor = 'order',points_to_label = 'all') # plot chart_plot(C,M[2]) # prepare model sequence MS = filter_smeta(       mode = 'include',        levels='QC',        factor_name = 'sample_type') +         filter_by_name(       mode = 'exclude',        dimension='sample',       names = c('1358BZU_0001QC_H1','1358BZU_0001QC_A1','1358BZU_0001QC_G1')) +         knn_impute(       neighbours=5) +         vec_norm() +          log_transform(        base = 10) +          mean_centre() +         PCA(        number_components = 3)  # apply model sequence MS = model_apply(MS, DE) ## Warning in knnimp(x, k, maxmiss = rowmax, maxp = maxp): 4 rows with more than 50 % entries missing; ##  mean imputation used for these rows # PCA scores plot C = pca_scores_plot(factor_name = 'sample_type',label_factor = 'order',points_to_label = 'all') # plot chart_plot(C,MS[7]) # prepare model sequence MS = filter_smeta(       mode = 'exclude',        levels='Blank',        factor_name = 'sample_type') +         filter_smeta(       mode = 'exclude',        levels='12',        factor_name = 'biol.batch') +         filter_by_name(       mode = 'exclude',        dimension='sample',       names = c('1358BZU_0001QC_H1',                 '1358BZU_0001QC_A1',                 '1358BZU_0001QC_G1')) +         knn_impute(       neighbours=5) +         vec_norm() +          log_transform(        base = 10) +          mean_centre() +         PCA(        number_components = 3)  # apply model sequence MS = model_apply(MS, DE) ## Warning in knnimp(x, k, maxmiss = rowmax, maxp = maxp): 2 rows with more than 50 % entries missing; ##  mean imputation used for these rows # PCA scores plots C = pca_scores_plot(factor_name = 'sample_type') # plot chart_plot(C,MS[8]) MS =  filter_smeta(        mode = 'exclude',         levels = '12',         factor_name = 'biol.batch') +          filter_by_name(        mode = 'exclude',         dimension='sample',        names = c('1358BZU_0001QC_H1',                  '1358BZU_0001QC_A1',                  '1358BZU_0001QC_G1')) +        blank_filter(        fold_change = 20,        qc_label = 'QC',        factor_name = 'sample_type') +        filter_smeta(        mode='exclude',        levels='Blank',        factor_name='sample_type') +          mv_feature_filter(        threshold = 80,         qc_label = 'QC',         factor_name = 'sample_type',         method = 'QC') +             mv_feature_filter(         threshold = 50,          factor_name = 'sample_type',          method='across') +         rsd_filter(        rsd_threshold=20,         qc_label='QC',        factor_name='sample_type') +         mv_sample_filter(        mv_threshold = 50) +            pqn_norm(        qc_label='QC',        factor_name='sample_type') +            knn_impute(        neighbours=5,         by='samples') +            glog_transform(        qc_label = 'QC',        factor_name = 'sample_type') +            mean_centre() +             PCA(        number_components = 10)  # apply model sequence MS = model_apply(MS, DE)   # PCA plots using different factors g=list() for (k in c('order','biol.batch','time.point','condition')) {   C = pca_scores_plot(factor_name = k,ellipse='none')   # plot   g[[k]]=chart_plot(C,MS[length(MS)]) }  plot_grid(plotlist = g,align='vh',axis='tblr',nrow=2,labels=c('A','B','C','D')) # get the glog scaled data GL = predicted(MS[11])  # extract the Ikaros group and apply PCA IK = filter_smeta(       mode='include',       factor_name='condition',       levels='Ikaros') +      mean_centre() +       PCA(number_components = 5)  # apply the model sequence to glog transformed data IK = model_apply(IK,GL)  # plot the PCA scores C = pca_scores_plot(factor_name='time.point',ellipse = 'sample') g1=chart_plot(C,IK[3]) g2=g1 + scale_color_viridis_d() # add continuous scale colouring  plot_grid(g1,g2,nrow=2,align='vh',axis = 'tblr',labels=c('A','B'))"},{"path":"https://computational-metabolomics.github.io/structToolbox/articles/data_analysis_omics_using_the_structtoolbox.html","id":"session-info","dir":"Articles","previous_headings":"","what":"Session Info","title":"Data analysis of metabolomics and other omics datasets using the structToolbox","text":"","code":"sessionInfo() ## R version 4.4.1 (2024-06-14) ## Platform: x86_64-pc-linux-gnu ## Running under: Ubuntu 22.04.4 LTS ##  ## Matrix products: default ## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3  ## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0 ##  ## locale: ##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               ##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8     ##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8    ##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                  ##  [9] LC_ADDRESS=C               LC_TELEPHONE=C             ## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C        ##  ## time zone: UTC ## tzcode source: system (glibc) ##  ## attached base packages: ## [1] stats     graphics  grDevices utils     datasets  methods   base      ##  ## other attached packages: ##  [1] openxlsx_4.2.5.2     cowplot_1.1.3        gridExtra_2.3        ##  [4] ggplot2_3.5.1        BiocFileCache_2.12.0 dbplyr_2.5.0         ##  [7] ropls_1.36.0         pmp_1.16.0           structToolbox_1.17.3 ## [10] struct_1.16.0        BiocStyle_2.32.1     ##  ## loaded via a namespace (and not attached): ##   [1] DBI_1.2.3                   MultiDataSet_1.32.0         ##   [3] httr2_1.0.1                 rlang_1.1.4                 ##   [5] magrittr_2.0.3              e1071_1.7-14                ##   [7] RSQLite_2.3.7               matrixStats_1.3.0           ##   [9] compiler_4.4.1              systemfonts_1.1.0           ##  [11] vctrs_0.6.5                 reshape2_1.4.4              ##  [13] stringr_1.5.1               pkgconfig_2.0.3             ##  [15] crayon_1.5.3                fastmap_1.2.0               ##  [17] XVector_0.44.0              labeling_0.4.3              ##  [19] utf8_1.2.4                  rmarkdown_2.27              ##  [21] UCSC.utils_1.0.0            itertools_0.1-3             ##  [23] ragg_1.3.2                  bit_4.0.5                   ##  [25] purrr_1.0.2                 xfun_0.45                   ##  [27] MultiAssayExperiment_1.30.2 randomForest_4.7-1.1        ##  [29] zlibbioc_1.50.0             cachem_1.1.0                ##  [31] GenomeInfoDb_1.40.1         jsonlite_1.8.8              ##  [33] blob_1.2.4                  highr_0.11                  ##  [35] DelayedArray_0.30.1         parallel_4.4.1              ##  [37] rols_3.0.0                  R6_2.5.1                    ##  [39] bslib_0.7.0                 stringi_1.8.4               ##  [41] limma_3.60.3                GenomicRanges_1.56.1        ##  [43] jquerylib_0.1.4             Rcpp_1.0.12                 ##  [45] bookdown_0.39               SummarizedExperiment_1.34.0 ##  [47] iterators_1.0.14            knitr_1.47                  ##  [49] IRanges_2.38.0              Matrix_1.7-0                ##  [51] tidyselect_1.2.1            abind_1.4-5                 ##  [53] yaml_2.3.8                  codetools_0.2-20            ##  [55] curl_5.2.1                  doRNG_1.8.6                 ##  [57] lattice_0.22-6              tibble_3.2.1                ##  [59] plyr_1.8.9                  withr_3.0.0                 ##  [61] Biobase_2.64.0              evaluate_0.24.0             ##  [63] ontologyIndex_2.12          desc_1.4.3                  ##  [65] isoband_0.2.7               proxy_0.4-27                ##  [67] zip_2.3.1                   filelock_1.0.3              ##  [69] pillar_1.9.0                BiocManager_1.30.23         ##  [71] MatrixGenerics_1.16.0       rngtools_1.5.2              ##  [73] foreach_1.5.2               stats4_4.4.1                ##  [75] generics_0.1.3              sp_2.1-4                    ##  [77] S4Vectors_0.42.0            munsell_0.5.1               ##  [79] scales_1.3.0                calibrate_1.7.7             ##  [81] class_7.3-22                glue_1.7.0                  ##  [83] tools_4.4.1                 fs_1.6.4                    ##  [85] grid_4.4.1                  impute_1.78.0               ##  [87] missForest_1.5              colorspace_2.1-0            ##  [89] GenomeInfoDbData_1.2.12     cli_3.6.3                   ##  [91] rappdirs_0.3.3              textshaping_0.4.0           ##  [93] fansi_1.0.6                 viridisLite_0.4.2           ##  [95] ggthemes_5.1.0              S4Arrays_1.4.1              ##  [97] dplyr_1.1.4                 pls_2.8-3                   ##  [99] pcaMethods_1.96.0           gtable_0.3.5                ## [101] sass_0.4.9                  digest_0.6.36               ## [103] BiocGenerics_0.50.0         SparseArray_1.4.8           ## [105] farver_2.1.2                htmlwidgets_1.6.4           ## [107] memoise_2.0.1               htmltools_0.5.8.1           ## [109] pkgdown_2.0.9.9000          lifecycle_1.0.4             ## [111] httr_1.4.7                  statmod_1.5.0               ## [113] bit64_4.0.5                 qqman_0.1.9                 ## [115] MASS_7.3-61"},{"path":"https://computational-metabolomics.github.io/structToolbox/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Gavin Rhys Lloyd. Author, maintainer. Ralf Johannes Maria Weber. Author.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Lloyd, G., Jankevics, ., Weber, R. 2020. struct: R/Bioconductor-based framework standardized metabolomics data analysis beyond. Bioinformatics, 36(22-23), p.5551-5552.","code":"@Article{,   title = {struct: an R/Bioconductor-based framework for standardized metabolomics data analysis and beyond},   author = {Gavin Rhys Lloyd and Andris Jankevics and Ralf Johannes Maria Weber},   journal = {Bioinformatics},   year = {2020},   volume = {36},   number = {22-23},   pages = {5551-5552},   url = {https://doi.org/10.1093/bioinformatics/btaa1031}, }"},{"path":"https://computational-metabolomics.github.io/structToolbox/index.html","id":"structtoolbox","dir":"","previous_headings":"","what":"Data processing & analysis tools for Metabolomics and other omics","title":"Data processing & analysis tools for Metabolomics and other omics","text":"extensive set data (pre-)processing analysis methods tools metabolomics omics, strong emphasis statistics machine learning. toolbox allows user build extensive standardised workflows data analysis. methods tools implemented using class-based templates provided struct (Statistics R Using Class-based Templates) package. toolbox includes pre-processing methods (e.g. signal drift batch correction, normalisation, missing value imputation scaling), univariate (e.g. ttest, various forms ANOVA, Kruskal–Wallis test ) multivariate statistical methods (e.g. PCA PLS, including cross-validation permutation testing) well machine learning methods (e.g. Support Vector Machines). STATistics Ontology (STATO) integrated implemented provide standardised definitions different methods, inputs outputs.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Data processing & analysis tools for Metabolomics and other omics","text":"install package: install development version:","code":"if (!require(\"BiocManager\", quietly = TRUE))     install.packages(\"BiocManager\")  BiocManager::install(\"structToolbox\") if (!require(\"remotes\", quietly = TRUE))     install.packages(\"remotes\")  remotes::install_github(\"computational-metabolomics/structToolbox\")"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ANOVA.html","id":null,"dir":"Reference","previous_headings":"","what":"Analysis of Variance — ANOVA","title":"Analysis of Variance — ANOVA","text":"Analysis Variance (ANOVA) univariate method used analyse difference among group means. Multiple test corrected p-values computed indicate significance feature.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ANOVA.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Analysis of Variance — ANOVA","text":"","code":"ANOVA(alpha = 0.05, mtc = \"fdr\", formula, ss_type = \"III\", ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ANOVA.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Analysis of Variance — ANOVA","text":"alpha (numeric) p-value cutoff determining significance. default 0.05. mtc (character) Multiple test correction method. Allowed values limited following: \"bonferroni\": Bonferroni correction p-values multiplied number comparisons. \"fdr\": Benjamini Hochberg False Discovery Rate correction. \"none\": correction. default \"fdr\". formula (formula) symbolic description model fitted. ss_type (character) ANOVA sum squares. Allowed values limited following: \"\": Type sum squares. \"II\": Type II sum squares. \"III\": Type III sum squares. default \"III\". ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ANOVA.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Analysis of Variance — ANOVA","text":" ANOVA object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ANOVA.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Analysis of Variance — ANOVA","text":"object makes use functionality following packages: car","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ANOVA.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Analysis of Variance — ANOVA","text":"ANOVA object inherits following struct classes: [ANOVA] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ANOVA.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Analysis of Variance — ANOVA","text":"Fox J, Weisberg S (2019). R Companion Applied Regression, Third edition. Sage, Thousand Oaks CA. https://socialsciences.mcmaster.ca/jfox/Books/Companion/.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ANOVA.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Analysis of Variance — ANOVA","text":"","code":"M = ANOVA(       alpha = 0.05,       mtc = \"fdr\",       formula = y ~ x,       ss_type = \"III\")  D = iris_DatasetExperiment() M = ANOVA(formula=y~Species) M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/AUC.html","id":null,"dir":"Reference","previous_headings":"","what":"Area under ROC curve — AUC","title":"Area under ROC curve — AUC","text":"area ROC curve classifier estimated using trapezoid method.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/AUC.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Area under ROC curve — AUC","text":"","code":"AUC(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/AUC.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Area under ROC curve — AUC","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/AUC.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Area under ROC curve — AUC","text":"  AUC  object. object output slots.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/AUC.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Area under ROC curve — AUC","text":"AUC object inherits following struct classes: [AUC] >> [metric] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/AUC.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Area under ROC curve — AUC","text":"","code":"M = AUC()  D = iris_DatasetExperiment() XCV = kfold_xval(folds=5,factor_name='Species') *       (mean_centre() + PLSDA(number_components=2,factor_name='Species')) MET = AUC() XCV = run(XCV,D,MET) #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors #> Warning: ‘>=’ not meaningful for factors"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DFA.html","id":null,"dir":"Reference","previous_headings":"","what":"Discriminant Factor Analysis — DFA","title":"Discriminant Factor Analysis — DFA","text":"Discriminant Factor Analysis (DFA) supervised classification method. Using linear combination input variables, DFA finds new orthogonal axes (canonical values) minimize variance within given class maximize variance classes.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DFA.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Discriminant Factor Analysis — DFA","text":"","code":"DFA(factor_name, number_components = 2, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DFA.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Discriminant Factor Analysis — DFA","text":"factor_name (character) name sample-meta column use. number_components (numeric, integer) number DFA components calculated. default 2. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DFA.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Discriminant Factor Analysis — DFA","text":" DFA object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DFA.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Discriminant Factor Analysis — DFA","text":"DFA object inherits following struct classes: [DFA] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DFA.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Discriminant Factor Analysis — DFA","text":"Manly B (1986). Multivariate Statistical Methods: Primer. Chapman Hall, Boca Raton.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DFA.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Discriminant Factor Analysis — DFA","text":"","code":"M = DFA(       factor_name = \"V1\",       number_components = 2)  D = iris_DatasetExperiment() M = DFA(factor_name='Species') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_boxplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Feature distribution histogram — DatasetExperiment_boxplot","title":"Feature distribution histogram — DatasetExperiment_boxplot","text":"boxplot visualise distribution values within subset features.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_boxplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Feature distribution histogram — DatasetExperiment_boxplot","text":"","code":"DatasetExperiment_boxplot(   factor_name,   by_sample = TRUE,   per_class = TRUE,   number = 50,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_boxplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Feature distribution histogram — DatasetExperiment_boxplot","text":"factor_name (character) name sample-meta column use. by_sample (logical) Plot sample. Allowed values limited following: \"TRUE\": data plotted across features subset samples. \"FALSE\": data plotted across samples subset features. default TRUE. per_class (logical) Plot per class. Allowed values limited following: \"TRUE\": data plotted class. \"FALSE\": data plotted samples. default TRUE. number (numeric, integer) number features/samples plotted. default 50. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_boxplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Feature distribution histogram — DatasetExperiment_boxplot","text":"  DatasetExperiment_boxplot  object. object output slots. See chart_plot struct package plot chart object. struct object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_boxplot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Feature distribution histogram — DatasetExperiment_boxplot","text":"DatasetExperiment_boxplot object inherits following struct classes: [DatasetExperiment_boxplot] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_boxplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Feature distribution histogram — DatasetExperiment_boxplot","text":"","code":"M = DatasetExperiment_boxplot(       factor_name = \"V1\",       by_sample = FALSE,       per_class = FALSE,       number = 50)  D = MTBLS79_DatasetExperiment() C = DatasetExperiment_boxplot(factor_name='Class',number=10,per_class=FALSE) chart_plot(C,D) #> Warning: Removed 585 rows containing non-finite outside the scale range #> (`stat_boxplot()`)."},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_dist.html","id":null,"dir":"Reference","previous_headings":"","what":"Feature distribution histogram — DatasetExperiment_dist","title":"Feature distribution histogram — DatasetExperiment_dist","text":"histogram visualise distribution values within features.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_dist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Feature distribution histogram — DatasetExperiment_dist","text":"","code":"DatasetExperiment_dist(factor_name, per_class = TRUE, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_dist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Feature distribution histogram — DatasetExperiment_dist","text":"factor_name (character) name sample-meta column use. per_class (logical) Plot per class. Allowed values limited following: \"TRUE\": distributions plotted class. \"FALSE\": distribution plotted samples. default TRUE. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_dist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Feature distribution histogram — DatasetExperiment_dist","text":"  DatasetExperiment_dist  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_dist.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Feature distribution histogram — DatasetExperiment_dist","text":"DatasetExperiment_dist object inherits following struct classes: [DatasetExperiment_dist] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_dist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Feature distribution histogram — DatasetExperiment_dist","text":"","code":"M = DatasetExperiment_dist(       factor_name = \"V1\",       per_class = FALSE)  D = MTBLS79_DatasetExperiment() C = DatasetExperiment_dist(factor_name='Class') chart_plot(C,D) #> Warning: Removed 9222 rows containing non-finite outside the scale range (`stat_bin()`)."},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_factor_boxplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Factor boxplot — DatasetExperiment_factor_boxplot","title":"Factor boxplot — DatasetExperiment_factor_boxplot","text":"Boxplot feature visualise distribution values within group","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_factor_boxplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Factor boxplot — DatasetExperiment_factor_boxplot","text":"","code":"DatasetExperiment_factor_boxplot(feature_to_plot, factor_names, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_factor_boxplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Factor boxplot — DatasetExperiment_factor_boxplot","text":"feature_to_plot (character, numeric, integer) name plotted feature. factor_names (character) name sample meta column(s) use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_factor_boxplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Factor boxplot — DatasetExperiment_factor_boxplot","text":"  DatasetExperiment_factor_boxplot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_factor_boxplot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Factor boxplot — DatasetExperiment_factor_boxplot","text":"DatasetExperiment_factor_boxplot object inherits following struct classes: [DatasetExperiment_factor_boxplot] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_factor_boxplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Factor boxplot — DatasetExperiment_factor_boxplot","text":"","code":"M = DatasetExperiment_factor_boxplot(       factor_names = \"V1\",       feature_to_plot = \"V1\")  D = iris_DatasetExperiment() C = DatasetExperiment_factor_boxplot(factor_names='Species',feature_to_plot='Petal.Width') chart_plot(C,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_heatmap.html","id":null,"dir":"Reference","previous_headings":"","what":"DatasetExperiment heatmap — DatasetExperiment_heatmap","title":"DatasetExperiment heatmap — DatasetExperiment_heatmap","text":"heatmap visualise measured values data matrix.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_heatmap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"DatasetExperiment heatmap — DatasetExperiment_heatmap","text":"","code":"DatasetExperiment_heatmap(na_colour = \"#FF00E4\", ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_heatmap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"DatasetExperiment heatmap — DatasetExperiment_heatmap","text":"na_colour (character) hex colour code used plot missing values. default \"#FF00E4\". ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_heatmap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"DatasetExperiment heatmap — DatasetExperiment_heatmap","text":"  DatasetExperiment_heatmap  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_heatmap.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"DatasetExperiment heatmap — DatasetExperiment_heatmap","text":"object makes use functionality following packages: reshape2","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_heatmap.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"DatasetExperiment heatmap — DatasetExperiment_heatmap","text":"DatasetExperiment_heatmap object inherits following struct classes: [DatasetExperiment_heatmap] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_heatmap.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"DatasetExperiment heatmap — DatasetExperiment_heatmap","text":"Wickham H (2007). \"Reshaping Data reshape Package.\" Journal Statistical Software, 21(12), 1-20. http://www.jstatsoft.org/v21/i12/.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/DatasetExperiment_heatmap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"DatasetExperiment heatmap — DatasetExperiment_heatmap","text":"","code":"M = DatasetExperiment_heatmap(       na_colour = \"#FF00E4\")  D = iris_DatasetExperiment() C = DatasetExperiment_heatmap() chart_plot(C,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HCA.html","id":null,"dir":"Reference","previous_headings":"","what":"Hierarchical Cluster Analysis — HCA","title":"Hierarchical Cluster Analysis — HCA","text":"Hierarchical Cluster Analysis numerical technique uses agglomerative clustering identify clusters groupings samples.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HCA.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hierarchical Cluster Analysis — HCA","text":"","code":"HCA(   dist_method = \"euclidean\",   cluster_method = \"complete\",   minkowski_power = 2,   factor_name,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HCA.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hierarchical Cluster Analysis — HCA","text":"dist_method (character) Distance measure. Allowed values limited following: \"euclidean\": euclidean distance (2 norm). \"maximum\": maximum distance. \"manhattan\": absolute distance (1 norm). \"canberra\": weighted version mahattan distance. \"minkowski\": generalisation manhattan euclidean distance nth norm. default \"euclidean\". cluster_method (character) Agglomeration method. Allowed values limited following: \"ward.D\": Ward clustering. \"ward.D2\": Ward clustering using sqaured distances. \"single\": Single linkage. \"complete\": Complete linkage. \"average\": Average linkage (UPGMA). \"mcquitty\": McQuitty linkage (WPGMA). \"median\": Median linkage (WPGMC). \"centroid\": Centroid linkage (UPGMC). default \"complete\". minkowski_power (numeric)  default 2. factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HCA.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Hierarchical Cluster Analysis — HCA","text":" HCA object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HCA.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Hierarchical Cluster Analysis — HCA","text":"object makes use functionality following packages: stats","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HCA.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Hierarchical Cluster Analysis — HCA","text":"HCA object inherits following struct classes: [HCA] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HCA.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Hierarchical Cluster Analysis — HCA","text":"R Core Team (2024). R: Language Environment Statistical Computing. R Foundation Statistical Computing, Vienna, Austria. https://www.R-project.org/.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HCA.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Hierarchical Cluster Analysis — HCA","text":"","code":"M = HCA(       dist_method = \"euclidean\",       cluster_method = \"complete\",       minkowski_power = numeric(0),       factor_name = \"V1\")  D = iris_DatasetExperiment() M = HCA(factor_name='Species') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSD.html","id":null,"dir":"Reference","previous_headings":"","what":"Tukey's Honest Significant Difference — HSD","title":"Tukey's Honest Significant Difference — HSD","text":"Tukey's HSD post hoc test modified t-test applied features pairs levels factor. used determine groups different (). multiple test corrected p-value computed indicate groups significantly different others feature.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSD.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tukey's Honest Significant Difference — HSD","text":"","code":"HSD(alpha = 0.05, mtc = \"fdr\", formula, unbalanced = FALSE, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSD.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tukey's Honest Significant Difference — HSD","text":"alpha (numeric) p-value cutoff determining significance. default 0.05. mtc (character) Multiple test correction method. Allowed values limited following: \"bonferroni\": Bonferroni correction p-values multiplied number comparisons. \"fdr\": Benjamini Hochberg False Discovery Rate correction. \"none\": correction. default \"fdr\". formula (formula) symbolic description model fitted. unbalanced (logical) Unbalanced model. Allowed values limited following: \"TRUE\": correction applied unbalanced designs. \"FALSE\": correction applied unbalanced designs. default FALSE. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSD.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tukey's Honest Significant Difference — HSD","text":" HSD object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSD.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tukey's Honest Significant Difference — HSD","text":"object makes use functionality following packages: agricolae","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSD.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Tukey's Honest Significant Difference — HSD","text":"HSD object inherits following struct classes: [HSD] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSD.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Tukey's Honest Significant Difference — HSD","text":"de Mendiburu F (2023). agricolae: Statistical Procedures Agricultural Research. R package version 1.3-7, https://CRAN.R-project.org/package=agricolae.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSD.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tukey's Honest Significant Difference — HSD","text":"","code":"M = HSD(       alpha = 0.05,       mtc = \"fdr\",       formula = y ~ x,       unbalanced = FALSE)  D = iris_DatasetExperiment() M = HSD(formula=y~Species) M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSDEM.html","id":null,"dir":"Reference","previous_headings":"","what":"Tukey's Honest Significant Difference using estimated marginal means — HSDEM","title":"Tukey's Honest Significant Difference using estimated marginal means — HSDEM","text":"Tukey's HSD post hoc test modified t-test applied features pairs levels factor. used determine groups different (). multiple test corrected p-value computed indicate groups significantly different others feature. mixed effects models estimated marginal means used.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSDEM.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tukey's Honest Significant Difference using estimated marginal means — HSDEM","text":"","code":"HSDEM(alpha = 0.05, mtc = \"fdr\", formula, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSDEM.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tukey's Honest Significant Difference using estimated marginal means — HSDEM","text":"alpha (numeric) p-value cutoff determining significance. default 0.05. mtc (character) Multiple test correction method. Allowed values limited following: \"bonferroni\": Bonferroni correction p-values multiplied number comparisons. \"fdr\": Benjamini Hochberg False Discovery Rate correction. \"none\": correction. default \"fdr\". formula (formula) symbolic description model fitted. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSDEM.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tukey's Honest Significant Difference using estimated marginal means — HSDEM","text":" HSDEM object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSDEM.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tukey's Honest Significant Difference using estimated marginal means — HSDEM","text":"object makes use functionality following packages: emmeans nlme","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSDEM.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Tukey's Honest Significant Difference using estimated marginal means — HSDEM","text":"HSDEM object inherits following struct classes: [HSDEM] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSDEM.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Tukey's Honest Significant Difference using estimated marginal means — HSDEM","text":"Lenth R (2024). emmeans: Estimated Marginal Means, aka Least-Squares Means. R package version 1.10.1, https://CRAN.R-project.org/package=emmeans. Pinheiro J, Bates D, R Core Team (2023). nlme: Linear Nonlinear Mixed Effects Models. R package version 3.1-164, https://CRAN.R-project.org/package=nlme. Pinheiro JC, Bates DM (2000). Mixed-Effects Models S S-PLUS. Springer, New York. doi:10.1007/b98882 https://doi.org/10.1007/b98882.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/HSDEM.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tukey's Honest Significant Difference using estimated marginal means — HSDEM","text":"","code":"M = HSDEM(       alpha = 0.05,       mtc = \"fdr\",       formula = y ~ x)  D = iris_DatasetExperiment() D$sample_meta$id=rownames(D) # dummy id column M = HSDEM(formula = y~Species+ Error(id/Species)) M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/MTBLS79_DatasetExperiment.html","id":null,"dir":"Reference","previous_headings":"","what":"MTBLS79: Direct infusion mass spectrometry metabolomics dataset: a benchmark for data processing and quality control — MTBLS79_DatasetExperiment","title":"MTBLS79: Direct infusion mass spectrometry metabolomics dataset: a benchmark for data processing and quality control — MTBLS79_DatasetExperiment","text":"Direct-infusion mass spectrometry (DIMS) metabolomics important approach characterising molecular responses organisms disease, drugs environment. Increasingly large-scale metabolomics studies conducted, necessitating improvements bioanalytical computational workflows maintain data quality. dataset represents systematic evaluation reproducibility multi-batch DIMS metabolomics study cardiac tissue extracts. comprises twenty biological samples (cow vs. sheep) analysed repeatedly, 8 batches across 7 days, together concurrent set quality control (QC) samples. Data presented step workflow available MetaboLights (https://www.ebi.ac.uk/metabolights/MTBLS79)","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/MTBLS79_DatasetExperiment.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MTBLS79: Direct infusion mass spectrometry metabolomics dataset: a benchmark for data processing and quality control — MTBLS79_DatasetExperiment","text":"","code":"MTBLS79_DatasetExperiment(filtered = FALSE)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/MTBLS79_DatasetExperiment.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"MTBLS79: Direct infusion mass spectrometry metabolomics dataset: a benchmark for data processing and quality control — MTBLS79_DatasetExperiment","text":"filtered TRUE load data quality control filters already applied, FALSE load unfiltered data. Default FALSE. raw data available (https://www.ebi.ac.uk/metabolights/MTBLS79) R dataset pmp package, available Bioconductor.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/MTBLS79_DatasetExperiment.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"MTBLS79: Direct infusion mass spectrometry metabolomics dataset: a benchmark for data processing and quality control — MTBLS79_DatasetExperiment","text":"DatasetExperiment object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/MTBLS79_DatasetExperiment.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"MTBLS79: Direct infusion mass spectrometry metabolomics dataset: a benchmark for data processing and quality control — MTBLS79_DatasetExperiment","text":"","code":"D = MTBLS79_DatasetExperiment() summary(D) #> [1] \"DatasetExperiment object of length 172 with 7 metadata columns\""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/OPLSDA.html","id":null,"dir":"Reference","previous_headings":"","what":"Orthogonal Partial Least Squares regression — OPLSDA","title":"Orthogonal Partial Least Squares regression — OPLSDA","text":"OPLS splits data matrix two parts. One part contains information orthogonal input vector, non-orthogonal.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/OPLSDA.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Orthogonal Partial Least Squares regression — OPLSDA","text":"","code":"OPLSDA(number_components = 1, factor_name, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/OPLSDA.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Orthogonal Partial Least Squares regression — OPLSDA","text":"number_components (numeric, integer) number orthgonal components. default 1. factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/OPLSDA.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Orthogonal Partial Least Squares regression — OPLSDA","text":" OPLSDA object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/OPLSDA.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Orthogonal Partial Least Squares regression — OPLSDA","text":"OPLSDA object inherits following struct classes: [OPLSDA] >> [OPLSR] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/OPLSDA.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Orthogonal Partial Least Squares regression — OPLSDA","text":"","code":"M = OPLSDA(       number_components = 2,       factor_name = \"V1\")  M = OPLSR('number_components'=2,factor_name='Species')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/OPLSR.html","id":null,"dir":"Reference","previous_headings":"","what":"Orthogonal Partial Least Squares regression — OPLSR","title":"Orthogonal Partial Least Squares regression — OPLSR","text":"OPLS splits data matrix two parts. One part contains information orthogonal input vector, non-orthogonal.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/OPLSR.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Orthogonal Partial Least Squares regression — OPLSR","text":"","code":"OPLSR(number_components = 2, factor_name, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/OPLSR.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Orthogonal Partial Least Squares regression — OPLSR","text":"number_components (numeric, integer) number orthgonal components. default 2. factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/OPLSR.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Orthogonal Partial Least Squares regression — OPLSR","text":" OPLSR object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/OPLSR.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Orthogonal Partial Least Squares regression — OPLSR","text":"OPLSR object inherits following struct classes: [OPLSR] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/OPLSR.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Orthogonal Partial Least Squares regression — OPLSR","text":"","code":"M = OPLSR(       number_components = 2,       factor_name = \"V1\")  M = OPLSR('number_components'=2,factor_name='Species')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PCA.html","id":null,"dir":"Reference","previous_headings":"","what":"Principal Component Analysis (PCA) — PCA","title":"Principal Component Analysis (PCA) — PCA","text":"PCA multivariate data reduction technique. summarises data smaller number Principal Components maximise variance.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PCA.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Principal Component Analysis (PCA) — PCA","text":"","code":"PCA(number_components = 2, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PCA.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Principal Component Analysis (PCA) — PCA","text":"number_components (numeric, integer) number Principal Components calculated. default 2. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PCA.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Principal Component Analysis (PCA) — PCA","text":" PCA object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PCA.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Principal Component Analysis (PCA) — PCA","text":"PCA object inherits following struct classes: [PCA] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PCA.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Principal Component Analysis (PCA) — PCA","text":"","code":"M = PCA(       number_components = 2)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSDA.html","id":null,"dir":"Reference","previous_headings":"","what":"Partial least squares discriminant analysis — PLSDA","title":"Partial least squares discriminant analysis — PLSDA","text":"PLS multivariate regression technique extracts latent variables maximising covariance input data response. Discriminant Analysis variant uses group labels response variable. >2 groups 1-vs-approach used. Group membership can predicted test samples based probability estimate group membership, estimated y-value.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSDA.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Partial least squares discriminant analysis — PLSDA","text":"","code":"PLSDA(number_components = 2, factor_name, pred_method = \"max_prob\", ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSDA.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Partial least squares discriminant analysis — PLSDA","text":"number_components (numeric, integer) number PLS components. default 2. factor_name (character) name sample-meta column use. pred_method (character) Prediction method. Allowed values limited following: \"max_yhat\": predicted group selected based largest value y_hat. \"max_prob\": predicted group selected based largest probability group membership. default \"max_prob\". ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSDA.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Partial least squares discriminant analysis — PLSDA","text":" PLSDA object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSDA.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Partial least squares discriminant analysis — PLSDA","text":"object makes use functionality following packages: pls","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSDA.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Partial least squares discriminant analysis — PLSDA","text":"PLSDA object inherits following struct classes: [PLSDA] >> [PLSR] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSDA.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Partial least squares discriminant analysis — PLSDA","text":"Liland K, Mevik B, Wehrens R (2023). pls: Partial Least Squares Principal Component Regression. R package version 2.8-3, https://CRAN.R-project.org/package=pls. Perez NF, Ferre J, Boque R (2009). \"Calculation reliability classification discriminant partial least-squares binary classification.\" Chemometrics Intelligent Laboratory Systems, 95(2), 122-128. Barker M, Rayens W (2003). \"Partial least squares discrimination.\" Journal Chemometrics, 17(3), 166-173.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSDA.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Partial least squares discriminant analysis — PLSDA","text":"","code":"M = PLSDA(       number_components = 2,       factor_name = \"V1\",       pred_method = \"max_prob\")  M = PLSDA('number_components'=2,factor_name='Species')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSR.html","id":null,"dir":"Reference","previous_headings":"","what":"Partial least squares regression — PLSR","title":"Partial least squares regression — PLSR","text":"PLS multivariate regression technique extracts latent variables maximising covariance input data response. regression response continuous variable.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSR.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Partial least squares regression — PLSR","text":"","code":"PLSR(number_components = 2, factor_name, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSR.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Partial least squares regression — PLSR","text":"number_components (numeric, integer) number PLS components. default 2. factor_name (character) name sample meta column(s) use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSR.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Partial least squares regression — PLSR","text":" PLSR object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSR.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Partial least squares regression — PLSR","text":"object makes use functionality following packages: pls","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSR.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Partial least squares regression — PLSR","text":"PLSR object inherits following struct classes: [PLSR] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSR.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Partial least squares regression — PLSR","text":"Liland K, Mevik B, Wehrens R (2023). pls: Partial Least Squares Principal Component Regression. R package version 2.8-3, https://CRAN.R-project.org/package=pls.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/PLSR.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Partial least squares regression — PLSR","text":"","code":"M = PLSR(       number_components = 2,       factor_name = \"V1\")  M = PLSR(factor_name='run_order')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/SVM.html","id":null,"dir":"Reference","previous_headings":"","what":"Support Vector Machine Classifier — SVM","title":"Support Vector Machine Classifier — SVM","text":"Support Vector Machines (SVM) machine learning algorithm classification. can make use kernel functions generate highly non-linear boundaries groups.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/SVM.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Support Vector Machine Classifier — SVM","text":"","code":"SVM(   factor_name,   kernel = \"linear\",   degree = 3,   gamma = 1,   coef0 = 0,   cost = 1,   class_weights = NULL,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/SVM.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Support Vector Machine Classifier — SVM","text":"factor_name (character) name sample-meta column use. kernel (character) Kernel type. Allowed values limited following: \"linear\": . \"polynomial\": . \"radial\": . \"sigmoid\": . default \"linear\". degree (numeric) polynomial degree. default 3. gamma (numeric) gamma parameter. default 1. coef0 (numeric) offset coefficient. default 0. cost (numeric) cost violating constraints. default 1. class_weights (numeric, character, NULL) named vector weights different classes.  Specifying \"inverse\" choose weights inversely proportional class distribution. default NULL. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/SVM.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Support Vector Machine Classifier — SVM","text":" SVM object following output slots: struct object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/SVM.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Support Vector Machine Classifier — SVM","text":"object makes use functionality following packages: e1071","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/SVM.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Support Vector Machine Classifier — SVM","text":"SVM object inherits following struct classes: [SVM] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/SVM.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Support Vector Machine Classifier — SVM","text":"Meyer D, Dimitriadou E, Hornik K, Weingessel , Leisch F (2023). e1071: Misc Functions Department Statistics, Probability Theory Group (Formerly: E1071), TU Wien. R package version 1.7-14, https://CRAN.R-project.org/package=e1071. Brereton RG, Lloyd GR (2010). \"Support Vector Machines classification regression.\" Analyst, 135(2), 230-267.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/SVM.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Support Vector Machine Classifier — SVM","text":"","code":"M = SVM(       factor_name = \"V1\",       kernel = \"linear\",       degree = 3,       gamma = 1,       coef0 = 0,       cost = 1,       class_weights = 1)  M = SVM(factor_name='Species',gamma=1)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/as_data_frame.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert to data.frame — as_data_frame","title":"Convert to data.frame — as_data_frame","text":"Convert outputs input model data.frame.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/as_data_frame.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert to data.frame — as_data_frame","text":"","code":"# S4 method for class 'filter_na_count' as_data_frame(M)  # S4 method for class 'ttest' as_data_frame(M)  # S4 method for class 'wilcox_test' as_data_frame(M)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/as_data_frame.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert to data.frame — as_data_frame","text":"M model object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/as_data_frame.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert to data.frame — as_data_frame","text":"data.frame model outputs","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/as_data_frame.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert to data.frame — as_data_frame","text":"","code":"D = iris_DatasetExperiment() M = filter_na_count(threshold=50,factor_name='Species') M= model_apply(M,D) df = as_data_frame(M)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/autoscale.html","id":null,"dir":"Reference","previous_headings":"","what":"Autoscaling — autoscale","title":"Autoscaling — autoscale","text":"variable/feature mean centred scaled standard deviation. transformed variables zero-mean unit-variance.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/autoscale.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Autoscaling — autoscale","text":"","code":"autoscale(mode = \"data\", ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/autoscale.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Autoscaling — autoscale","text":"mode (character) Mode action. Allowed values limited following: \"data\": Autoscaling applied data matrix . \"sample_meta\": Autoscaling applied sample_meta data . \"\": Autoscaling applied data matrix meta data. default \"data\". ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/autoscale.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Autoscaling — autoscale","text":" autoscale object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/autoscale.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Autoscaling — autoscale","text":"autoscale object inherits following struct classes: [autoscale] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/autoscale.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Autoscaling — autoscale","text":"","code":"M = autoscale(       mode = \"data\")  D = iris_DatasetExperiment() M = autoscale() M = model_train(M,D) M = model_predict(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/balanced_accuracy.html","id":null,"dir":"Reference","previous_headings":"","what":"Balanced Accuracy — balanced_accuracy","title":"Balanced Accuracy — balanced_accuracy","text":"Balanced Accuracy average proportion correctly classified samples across groups.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/balanced_accuracy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Balanced Accuracy — balanced_accuracy","text":"","code":"balanced_accuracy(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/balanced_accuracy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Balanced Accuracy — balanced_accuracy","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/balanced_accuracy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Balanced Accuracy — balanced_accuracy","text":"  balanced_accuracy  object. object output slots.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/balanced_accuracy.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Balanced Accuracy — balanced_accuracy","text":"balanced_accuracy object inherits following struct classes: [balanced_accuracy] >> [metric] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/balanced_accuracy.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Balanced Accuracy — balanced_accuracy","text":"","code":"M = balanced_accuracy()  D = iris_DatasetExperiment() XCV = kfold_xval(folds=5,factor_name='Species') *       (mean_centre() + PLSDA(number_components=2,factor_name='Species')) MET = balanced_accuracy() XCV = run(XCV,D,MET)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/blank_filter.html","id":null,"dir":"Reference","previous_headings":"","what":"Blank filter — blank_filter","title":"Blank filter — blank_filter","text":"blank filter filters features comparing median intensity blank samples median intensity samples. Features relative intensity (fold change) large compared blank removed. number times feature detected across blank samples may also considered. feature detected high enough proportion blanks removed.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/blank_filter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Blank filter — blank_filter","text":"","code":"blank_filter(   fold_change = 20,   blank_label = \"blank\",   qc_label = \"QC\",   factor_name,   fraction_in_blank = 0,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/blank_filter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Blank filter — blank_filter","text":"fold_change (numeric) Features fold change less value removed. default 20. blank_label (character) label used identify blank samples. default \"blank\". qc_label (character, NULL) label used identify QC samples. set NULL median samples used. default \"QC\". factor_name (character) name sample-meta column use. fraction_in_blank (numeric) Features present less proportion blanks considered removal. default 0. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/blank_filter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Blank filter — blank_filter","text":" blank_filter object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/blank_filter.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Blank filter — blank_filter","text":"object makes use functionality following packages: pmp","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/blank_filter.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Blank filter — blank_filter","text":"blank_filter object inherits following struct classes: [blank_filter] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/blank_filter.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Blank filter — blank_filter","text":"Jankevics , Lloyd GR, Weber RJM (????). pmp: Peak Matrix Processing signal batch correction metabolomics datasets. R package version 1.15.1.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/blank_filter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Blank filter — blank_filter","text":"","code":"M = blank_filter(       fold_change = 20,       blank_label = \"Blank\",       qc_label = \"QC\",       factor_name = \"V1\",       fraction_in_blank = 0)  D = iris_DatasetExperiment() M = blank_filter(fold_change=2,                  factor_name='Species',                  blank_label='setosa',                  qc_label='versicolor') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/blank_filter_hist.html","id":null,"dir":"Reference","previous_headings":"","what":"Histogram of blank filter fold changes — blank_filter_hist","title":"Histogram of blank filter fold changes — blank_filter_hist","text":"histogram calculated fold changes blank filter (median samples divided median blanks)","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/blank_filter_hist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Histogram of blank filter fold changes — blank_filter_hist","text":"","code":"blank_filter_hist(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/blank_filter_hist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Histogram of blank filter fold changes — blank_filter_hist","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/blank_filter_hist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Histogram of blank filter fold changes — blank_filter_hist","text":"  blank_filter_hist  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/blank_filter_hist.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Histogram of blank filter fold changes — blank_filter_hist","text":"blank_filter_hist object inherits following struct classes: [blank_filter_hist] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/blank_filter_hist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Histogram of blank filter fold changes — blank_filter_hist","text":"","code":"M = blank_filter_hist()  C = blank_filter_hist()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/bootstrap.html","id":null,"dir":"Reference","previous_headings":"","what":"Bootstrap resampling — bootstrap","title":"Bootstrap resampling — bootstrap","text":"bootstrap resampling subset samples selected random replacement form training set. sample selected training included test set. process repeated many times, performance metrics computed repetition.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/bootstrap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bootstrap resampling — bootstrap","text":"","code":"bootstrap(number_of_repetitions = 100, collect, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/bootstrap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bootstrap resampling — bootstrap","text":"number_of_repetitions (numeric, integer) number bootstrap repetitions. default 100. collect (character) name model output collect bootstrap repetitions, addition input metric. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/bootstrap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bootstrap resampling — bootstrap","text":" bootstrap object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/bootstrap.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Bootstrap resampling — bootstrap","text":"bootstrap object inherits following struct classes: [bootstrap] >> [resampler] >> [iterator] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/bootstrap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bootstrap resampling — bootstrap","text":"","code":"M = bootstrap(       number_of_repetitions = 10,       collect = \"vip\")  I = bootstrap(number_of_repetitions = 10, collect = 'vip')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/calculate.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate metric — calculate,AUC-method","title":"Calculate metric — calculate,AUC-method","text":"Calculate metric","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/calculate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate metric — calculate,AUC-method","text":"","code":"# S4 method for class 'AUC' calculate(obj, Y, Yhat)  # S4 method for class 'balanced_accuracy' calculate(obj, Y, Yhat)  # S4 method for class 'r_squared' calculate(obj, Y, Yhat)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/calculate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate metric — calculate,AUC-method","text":"obj metric object Y true values/group labels Yhat predicted values/group labels","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/calculate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate metric — calculate,AUC-method","text":"modified metric object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/calculate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate metric — calculate,AUC-method","text":"","code":"MET = metric() calculate(MET) #> Warning: no calculation provided for this metric #> A \"metric\" object #> ----------------- #> name:           #> description:    #> value:          #>"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/chart_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"chart_plot method — chart_plot,dfa_scores_plot,DFA-method","title":"chart_plot method — chart_plot,dfa_scores_plot,DFA-method","text":"Plots chart object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/chart_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"chart_plot method — chart_plot,dfa_scores_plot,DFA-method","text":"","code":"# S4 method for class 'dfa_scores_plot,DFA' chart_plot(obj, dobj)  # S4 method for class 'scatter_chart,DatasetExperiment' chart_plot(obj, dobj)  # S4 method for class 'pca_correlation_plot,PCA' chart_plot(obj, dobj)  # S4 method for class 'pca_scores_plot,PCA' chart_plot(obj, dobj)  # S4 method for class 'pca_biplot,PCA' chart_plot(obj, dobj)  # S4 method for class 'pca_loadings_plot,PCA' chart_plot(obj, dobj)  # S4 method for class 'pca_scree_plot,PCA' chart_plot(obj, dobj)  # S4 method for class 'pca_dstat_plot,PCA' chart_plot(obj, dobj)  # S4 method for class 'plsr_prediction_plot,PLSR' chart_plot(obj, dobj)  # S4 method for class 'plsr_residual_hist,PLSR' chart_plot(obj, dobj)  # S4 method for class 'plsr_qq_plot,PLSR' chart_plot(obj, dobj)  # S4 method for class 'plsr_cook_dist,PLSR' chart_plot(obj, dobj)  # S4 method for class 'pls_scores_plot,PLSR' chart_plot(obj, dobj)  # S4 method for class 'plsda_predicted_plot,PLSDA' chart_plot(obj, dobj)  # S4 method for class 'plsda_roc_plot,PLSDA' chart_plot(obj, dobj)  # S4 method for class 'pls_vip_plot,PLSR' chart_plot(obj, dobj)  # S4 method for class 'pls_regcoeff_plot,PLSR' chart_plot(obj, dobj)  # S4 method for class 'blank_filter_hist,blank_filter' chart_plot(obj, dobj)  # S4 method for class 'confounders_lsq_barchart,confounders_clsq' chart_plot(obj, dobj)  # S4 method for class 'confounders_lsq_boxplot,confounders_clsq' chart_plot(obj, dobj)  # S4 method for class 'feature_boxplot,DatasetExperiment' chart_plot(obj, dobj)  # S4 method for class 'mv_histogram,DatasetExperiment' chart_plot(obj, dobj)  # S4 method for class 'mv_boxplot,DatasetExperiment' chart_plot(obj, dobj)  # S4 method for class 'DatasetExperiment_dist,DatasetExperiment' chart_plot(obj, dobj)  # S4 method for class 'DatasetExperiment_boxplot,DatasetExperiment' chart_plot(obj, dobj)  # S4 method for class 'compare_dist,DatasetExperiment' chart_plot(obj, dobj, eobj)  # S4 method for class 'DatasetExperiment_heatmap,DatasetExperiment' chart_plot(obj, dobj)  # S4 method for class 'DatasetExperiment_factor_boxplot,DatasetExperiment' chart_plot(obj, dobj)  # S4 method for class 'feature_profile_array,DatasetExperiment' chart_plot(obj, dobj)  # S4 method for class 'feature_profile,DatasetExperiment' chart_plot(obj, dobj)  # S4 method for class 'fold_change_plot,fold_change' chart_plot(obj, dobj)  # S4 method for class 'fs_line,forward_selection_by_rank' chart_plot(obj, dobj)  # S4 method for class 'glog_opt_plot,glog_transform' chart_plot(obj, dobj, gobj)  # S4 method for class 'gs_line,grid_search_1d' chart_plot(obj, dobj)  # S4 method for class 'hca_dendrogram,HCA' chart_plot(obj, dobj)  # S4 method for class 'kfoldxcv_grid,kfold_xval' chart_plot(obj, dobj)  # S4 method for class 'kfoldxcv_metric,kfold_xval' chart_plot(obj, dobj)  # S4 method for class 'kw_p_hist,kw_rank_sum' chart_plot(obj, dobj)  # S4 method for class 'mv_feature_filter_hist,mv_feature_filter' chart_plot(obj, dobj)  # S4 method for class 'mv_sample_filter_hist,mv_sample_filter' chart_plot(obj, dobj)  # S4 method for class 'permutation_test_plot,permutation_test' chart_plot(obj, dobj)  # S4 method for class 'plsda_feature_importance_plot,PLSDA' chart_plot(obj, dobj)  # S4 method for class 'pqn_norm_hist,pqn_norm' chart_plot(obj, dobj)  # S4 method for class 'resample_chart,resample' chart_plot(obj, dobj)  # S4 method for class 'rsd_filter_hist,rsd_filter' chart_plot(obj, dobj)  # S4 method for class 'feature_profile,sb_corr' chart_plot(obj, dobj, gobj)  # S4 method for class 'svm_plot_2d,SVM' chart_plot(obj, dobj, gobj)  # S4 method for class 'tSNE_scatter,tSNE' chart_plot(obj, dobj)  # S4 method for class 'tic_chart,DatasetExperiment' chart_plot(obj, dobj)  # S4 method for class 'wilcox_p_hist,wilcox_test' chart_plot(obj, dobj)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/chart_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"chart_plot method — chart_plot,dfa_scores_plot,DFA-method","text":"obj chart object dobj struct object eobj second DatasetExperiment object compare first gobj DatasetExperiment object signal correction applied.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/chart_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"chart_plot method — chart_plot,dfa_scores_plot,DFA-method","text":"plot object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/chart_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"chart_plot method — chart_plot,dfa_scores_plot,DFA-method","text":"","code":"C = example_chart() chart_plot(C,iris_DatasetExperiment()) #> Warning: no chart defined for \"DatasetExperiment\" #> A \"example_chart\" object #> ------------------------ #> name:           #> description:"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/classical_lsq.html","id":null,"dir":"Reference","previous_headings":"","what":"Univariate Classical Least Squares Regression — classical_lsq","title":"Univariate Classical Least Squares Regression — classical_lsq","text":"univariate classical least squares regression line fitted feature/variable response variable. fitted line minimises sum squared differences true response predicted response. coefficients (offset, gradient) fit can tested significance.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/classical_lsq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Univariate Classical Least Squares Regression — classical_lsq","text":"","code":"classical_lsq(alpha = 0.05, mtc = \"fdr\", factor_names, intercept = TRUE, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/classical_lsq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Univariate Classical Least Squares Regression — classical_lsq","text":"alpha (numeric) p-value cutoff determining significance. default 0.05. mtc (character) Multiple test correction method. Allowed values limited following: \"bonferroni\": Bonferroni correction p-values multiplied number comparisons. \"fdr\": Benjamini Hochberg False Discovery Rate correction. \"none\": correction. default \"fdr\". factor_names (character, list) column names regress . character vector list used ofr features. list character vectors provided assumed different set columns feature. intercept (logical) Model intercept. Allowed values limited following: \"TRUE\": intercept term included model. \"FALSE\": intercept term included model. default TRUE. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/classical_lsq.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Univariate Classical Least Squares Regression — classical_lsq","text":" classical_lsq object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/classical_lsq.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Univariate Classical Least Squares Regression — classical_lsq","text":"classical_lsq object inherits following struct classes: [classical_lsq] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/classical_lsq.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Univariate Classical Least Squares Regression — classical_lsq","text":"","code":"M = classical_lsq(       alpha = 0.05,       mtc = \"fdr\",       factor_names = \"V1\",       intercept = FALSE)  D = iris_DatasetExperiment() M = classical_lsq(factor_names = 'Species') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/compare_dist.html","id":null,"dir":"Reference","previous_headings":"","what":"Compare distributions — compare_dist","title":"Compare distributions — compare_dist","text":"Histograms boxplots computed across samples features used visually compare two datasets e.g. filtering /normalisation.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/compare_dist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compare distributions — compare_dist","text":"","code":"compare_dist(factor_name, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/compare_dist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compare distributions — compare_dist","text":"factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/compare_dist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compare distributions — compare_dist","text":"  compare_dist  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/compare_dist.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Compare distributions — compare_dist","text":"compare_dist object inherits following struct classes: [compare_dist] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/compare_dist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compare distributions — compare_dist","text":"","code":"M = compare_dist(       factor_name = \"V1\")  D1=MTBLS79_DatasetExperiment(filtered=FALSE) D2=MTBLS79_DatasetExperiment(filtered=TRUE) C = compare_dist(factor_name='Class') chart_plot(C,D1,D2) #> Warning: Removed 109 rows containing non-finite outside the scale range #> (`stat_boxplot()`). #> Warning: Removed 109 rows containing non-finite outside the scale range #> (`stat_boxplot()`). #> Warning: Removed 8012 rows containing non-finite outside the scale range (`stat_bin()`). #> Warning: Removed 8012 rows containing non-finite outside the scale range (`stat_bin()`). #> Warning: Removed 10623 rows containing non-finite outside the scale range #> (`stat_bin()`). #> Warning: Removed 6 rows containing missing values or values outside the scale range #> (`geom_path()`). #> Warning: Removed 10623 rows containing non-finite outside the scale range #> (`stat_bin()`). #> Warning: Removed 6 rows containing missing values or values outside the scale range #> (`geom_path()`). #> Warning: Removed 109 rows containing non-finite outside the scale range #> (`stat_boxplot()`). #> Warning: Removed 109 rows containing non-finite outside the scale range #> (`stat_boxplot()`).  #> TableGrob (4 x 2) \"arrange\": 4 grobs #>   z     cells    name           grob #> 1 1 (1-1,1-1) arrange gtable[layout] #> 2 2 (1-1,2-2) arrange gtable[layout] #> 3 3 (2-4,1-1) arrange gtable[layout] #> 4 4 (2-4,2-2) arrange gtable[layout]"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_clsq.html","id":null,"dir":"Reference","previous_headings":"","what":"Check for confounding factors — confounders_clsq","title":"Check for confounding factors — confounders_clsq","text":"Univariate least squares regression models used compare models without potential confounding factors included. change coefficients (delta) computed potential confounding factor. Factors large delta said large impact model therefore confounding. p-values computed models confounders included reduce potential false positives. suitable main factors 2 levels.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_clsq.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check for confounding factors — confounders_clsq","text":"","code":"confounders_clsq(   alpha = 0.05,   mtc = \"fdr\",   factor_name,   confounding_factors,   threshold = 0.15,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_clsq.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check for confounding factors — confounders_clsq","text":"alpha (numeric) p-value cutoff determining significance. default 0.05. mtc (character) Multiple test correction method. Allowed values limited following: \"bonferroni\": Bonferroni correction p-values multiplied number comparisons. \"fdr\": Benjamini Hochberg False Discovery Rate correction. \"none\": correction. default \"fdr\". factor_name (character) name main factor factors may confounding. confounding_factors (character) name(s) factor(s) potential confounding factors. threshold (numeric) Factors delta greater threshold considered confounding. default 0.15. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_clsq.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check for confounding factors — confounders_clsq","text":" confounders_clsq object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_clsq.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Check for confounding factors — confounders_clsq","text":"confounders_clsq object inherits following struct classes: [confounders_clsq] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_clsq.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check for confounding factors — confounders_clsq","text":"","code":"M = confounders_clsq(       alpha = 0.05,       mtc = \"fdr\",       factor_name = character(0),       confounding_factors = character(0),       threshold = 0.15)  D = MTBLS79_DatasetExperiment() M = filter_by_name(mode='include',dimension='variable',         names=colnames(D$data)[1:10]) + # first 10 features     filter_smeta(mode='exclude',levels='QC',         factor_name='Class') + # reduce to two group comparison     confounders_clsq(factor_name = 'Class',         confounding_factors=c('run_order','Batch')) M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_lsq_barchart.html","id":null,"dir":"Reference","previous_headings":"","what":"Confounding factor relative change barchart — confounders_lsq_barchart","title":"Confounding factor relative change barchart — confounders_lsq_barchart","text":"barchart relative change (delta) regression coefficient potential confounding factors included, excluded, model. Factors large delta considered confounding factors.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_lsq_barchart.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Confounding factor relative change barchart — confounders_lsq_barchart","text":"","code":"confounders_lsq_barchart(feature_to_plot, threshold = 10, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_lsq_barchart.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Confounding factor relative change barchart — confounders_lsq_barchart","text":"feature_to_plot (numeric, character, integer) column name feature plotted. threshold (numeric) horizontal line plotted indicate threshold. default 10. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_lsq_barchart.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Confounding factor relative change barchart — confounders_lsq_barchart","text":"  confounders_lsq_barchart  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_lsq_barchart.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Confounding factor relative change barchart — confounders_lsq_barchart","text":"confounders_lsq_barchart object inherits following struct classes: [confounders_lsq_barchart] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_lsq_barchart.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Confounding factor relative change barchart — confounders_lsq_barchart","text":"","code":"M = confounders_lsq_barchart(       feature_to_plot = 1,       threshold = 10)  D = MTBLS79_DatasetExperiment() M = filter_by_name(mode='include',dimension='variable',         names=colnames(D$data)[1:10]) + # first 10 features     filter_smeta(mode='exclude',levels='QC',         factor_name='Class') + # reduce to two group comparison     confounders_clsq(factor_name = 'Class',         confounding_factors=c('run_order','Batch')) M = model_apply(M,D) C = C=confounders_lsq_barchart(feature_to_plot=1,threshold=15) chart_plot(C,M[3])"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_lsq_boxplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Confounding factor relative change boxplot — confounders_lsq_boxplot","title":"Confounding factor relative change boxplot — confounders_lsq_boxplot","text":"boxplot relative change (delta) regression coefficient potential confounding factors included, excluded, model. Factors large delta considered confounding factors.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_lsq_boxplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Confounding factor relative change boxplot — confounders_lsq_boxplot","text":"","code":"confounders_lsq_boxplot(threshold = 10, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_lsq_boxplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Confounding factor relative change boxplot — confounders_lsq_boxplot","text":"threshold (numeric) horizontal line plotted indicate threshold. default 10. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_lsq_boxplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Confounding factor relative change boxplot — confounders_lsq_boxplot","text":"  confounders_lsq_boxplot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_lsq_boxplot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Confounding factor relative change boxplot — confounders_lsq_boxplot","text":"confounders_lsq_boxplot object inherits following struct classes: [confounders_lsq_boxplot] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/confounders_lsq_boxplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Confounding factor relative change boxplot — confounders_lsq_boxplot","text":"","code":"M = confounders_lsq_boxplot(       threshold = 10)  D = MTBLS79_DatasetExperiment() M = filter_by_name(mode='include',dimension='variable',         names=colnames(D$data)[1:10]) + # first 10 features     filter_smeta(mode='exclude',levels='QC',         factor_name='Class') + # reduce to two group comparison     confounders_clsq(factor_name = 'Class',         confounding_factors=c('run_order','Batch')) M = model_apply(M,D) C = C=confounders_lsq_boxplot(threshold=15) chart_plot(C,M[3])"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/constant_sum_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"Normalisation to constant sum — constant_sum_norm","title":"Normalisation to constant sum — constant_sum_norm","text":"sample normalised total signal equal one (scaling factor specified).","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/constant_sum_norm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Normalisation to constant sum — constant_sum_norm","text":"","code":"constant_sum_norm(scaling_factor = 1, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/constant_sum_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Normalisation to constant sum — constant_sum_norm","text":"scaling_factor (numeric) scaling factor applied normalisation. default 1. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/constant_sum_norm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Normalisation to constant sum — constant_sum_norm","text":" constant_sum_norm object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/constant_sum_norm.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Normalisation to constant sum — constant_sum_norm","text":"constant_sum_norm object inherits following struct classes: [constant_sum_norm] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/constant_sum_norm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Normalisation to constant sum — constant_sum_norm","text":"","code":"M = constant_sum_norm(       scaling_factor = 1)  M = constant_sum_norm()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/corr_coef.html","id":null,"dir":"Reference","previous_headings":"","what":"Correlation coefficient — corr_coef","title":"Correlation coefficient — corr_coef","text":"correlation features set continuous factor calculated. Multiple-test corrected p-values used indicate whether computed coefficients may occurred chance.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/corr_coef.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Correlation coefficient — corr_coef","text":"","code":"corr_coef(alpha = 0.05, mtc = \"fdr\", factor_names, method = \"spearman\", ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/corr_coef.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Correlation coefficient — corr_coef","text":"alpha (numeric) p-value cutoff determining significance. default 0.05. mtc (character) Multiple test correction method. Allowed values limited following: \"bonferroni\": Bonferroni correction p-values multiplied number comparisons. \"fdr\": Benjamini Hochberg False Discovery Rate correction. \"none\": correction. default \"fdr\". factor_names (character) name sample meta column(s) use. method (character) Type correlation. Allowed values limited following: \"kendall\": Kendall's tau computed. \"pearson\": Pearson product moment correlation computed. \"spearman\": Spearman's rho statistic computed. default \"spearman\". ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/corr_coef.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Correlation coefficient — corr_coef","text":" corr_coef object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/corr_coef.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Correlation coefficient — corr_coef","text":"object makes use functionality following packages: stats","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/corr_coef.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Correlation coefficient — corr_coef","text":"corr_coef object inherits following struct classes: [corr_coef] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/corr_coef.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Correlation coefficient — corr_coef","text":"R Core Team (2024). R: Language Environment Statistical Computing. R Foundation Statistical Computing, Vienna, Austria. https://www.R-project.org/.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/corr_coef.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Correlation coefficient — corr_coef","text":"","code":"M = corr_coef(       alpha = 0.05,       mtc = \"fdr\",       factor_names = \"V1\",       method = \"spearman\")  D = MTBLS79_DatasetExperiment(filtered=TRUE)  # subset for this example D = D[,1:10]  # convert to numeric for this example D$sample_meta$sample_order=as.numeric(D$sample_meta$run_order) D$sample_meta$sample_rep=as.numeric(D$sample_meta$Sample_Rep)  M = corr_coef(factor_names=c('sample_order','sample_rep')) M = model_apply(M,D) #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: Cannot compute exact p-value with ties #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion #> Warning: NAs introduced by coercion"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dfa_scores_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"DFA scores plot — dfa_scores_plot","title":"DFA scores plot — dfa_scores_plot","text":"scatter plot selected DFA components.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dfa_scores_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"DFA scores plot — dfa_scores_plot","text":"","code":"dfa_scores_plot(   components = c(1, 2),   points_to_label = \"none\",   factor_name,   ellipse = \"all\",   label_filter = character(0),   label_factor = \"rownames\",   label_size = 3.88,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dfa_scores_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"DFA scores plot — dfa_scores_plot","text":"components (numeric) components selected plotting. default c(1, 2). points_to_label (character) Points label. Allowed values limited following: \"none\": samples labels displayed. \"\": labels samples displayed. \"outliers\": Labels potential outlier samples displayed. default \"none\". factor_name (character) name sample-meta column use. ellipse (character) Plot ellipses. Allowed values limited following: \"\": Hotelling T2 ellipses (p=0.95) plotted groups samples. \"group\": Hotelling T2 ellipses (p=0.95) plotted groups. \"none\": Ellipses included plot. \"sample\": Hotelling T2 ellipse (p=0.95) plotted samples (ignoring group). default \"\". label_filter (character) Labels plotted named groups. zero-length groups included. default character(0). label_factor (character) column name sample_meta use labelling samples plot. \"rownames\" use row names sample_meta. default \"rownames\". label_size (numeric) text size labels. Note Font Units. default 3.88. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dfa_scores_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"DFA scores plot — dfa_scores_plot","text":"  dfa_scores_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dfa_scores_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"DFA scores plot — dfa_scores_plot","text":"object makes use functionality following packages: scales ggplot2","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dfa_scores_plot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"DFA scores plot — dfa_scores_plot","text":"dfa_scores_plot object inherits following struct classes: [dfa_scores_plot] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dfa_scores_plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"DFA scores plot — dfa_scores_plot","text":"Wickham H, Pedersen T, Seidel D (2023). scales: Scale Functions Visualization. R package version 1.3.0, https://CRAN.R-project.org/package=scales. Wickham H (2016). ggplot2: Elegant Graphics Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dfa_scores_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"DFA scores plot — dfa_scores_plot","text":"","code":"M = dfa_scores_plot(       components = c(1, 2),       points_to_label = \"none\",       factor_name = \"V1\",       ellipse = \"all\",       label_filter = character(0),       label_factor = \"rownames\",       label_size = 3.88)  D = iris_DatasetExperiment() M = mean_centre() + DFA(factor_name='Species') M = model_apply(M,D) C = dfa_scores_plot(factor_name = 'Species') chart_plot(C,M[2]) #> Warning: The following aesthetics were dropped during statistical transformation: label. #> ℹ This can happen when ggplot fails to infer the correct grouping structure in #>   the data. #> ℹ Did you forget to specify a `group` aesthetic or to convert a numerical #>   variable into a factor? #> Warning: The following aesthetics were dropped during statistical transformation: label. #> ℹ This can happen when ggplot fails to infer the correct grouping structure in #>   the data. #> ℹ Did you forget to specify a `group` aesthetic or to convert a numerical #>   variable into a factor? #> Warning: The following aesthetics were dropped during statistical transformation: label. #> ℹ This can happen when ggplot fails to infer the correct grouping structure in #>   the data. #> ℹ Did you forget to specify a `group` aesthetic or to convert a numerical #>   variable into a factor? #> Warning: The following aesthetics were dropped during statistical transformation: label. #> ℹ This can happen when ggplot fails to infer the correct grouping structure in #>   the data. #> ℹ Did you forget to specify a `group` aesthetic or to convert a numerical #>   variable into a factor?"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dratio_filter.html","id":null,"dir":"Reference","previous_headings":"","what":"Dispersion ratio filter — dratio_filter","title":"Dispersion ratio filter — dratio_filter","text":"dispersion ratio (d-ratio) compares standard deviation (non-parametric equivalent) Quality Control (QC) samples relative standard deviation (non-parametric equivalent) samples feature. d-ratio greater predefined threshold observed sample variance due technical variance feature removed.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dratio_filter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dispersion ratio filter — dratio_filter","text":"","code":"dratio_filter(   threshold = 20,   qc_label = \"QC\",   factor_name,   method = \"ratio\",   dispersion = \"sd\",   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dratio_filter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dispersion ratio filter — dratio_filter","text":"threshold (numeric) threshold features removed. default 20. qc_label (character) label used identify QC samples. default \"QC\". factor_name (character) name sample-meta column use. method (character) dratio method. Allowed values limited following: \"ratio\": Dispersion QCs divided dispersion samples. Corresponds Eq 4  Broadhurst et al (2018). \"euclidean\": Dispersion QCs divided euclidean length total dispersion. Total dispersion estimated QC Sample dispersion assuming orthogonal. Corresponds Eq 5 Broadhurst et al (2018). default \"ratio\". dispersion (character) Dispersion method. Allowed values limited following: \"sd\": Dispersion estimated using standard deviation. \"mad\": Dispersion estimated using median absolute deviation. default \"sd\". ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dratio_filter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Dispersion ratio filter — dratio_filter","text":" dratio_filter object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dratio_filter.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Dispersion ratio filter — dratio_filter","text":"dratio_filter object inherits following struct classes: [dratio_filter] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dratio_filter.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Dispersion ratio filter — dratio_filter","text":"Broadhurst D, Goodacre R, Reinke SN, Kuligowski J, Wilson ID, Lewis MR, Dunn WB (2018). \"Guidelines considerations use system suitability quality control samples mass spectrometry assays applied untargeted clinical metabolomic studies.\" Metabolomics, 14(6).","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/dratio_filter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Dispersion ratio filter — dratio_filter","text":"","code":"M = dratio_filter(       threshold = 20,       qc_label = \"QC\",       factor_name = \"V1\",       method = \"ratio\",       dispersion = \"sd\")  D = MTBLS79_DatasetExperiment() M = dratio_filter(threshold=20,qc_label='QC',factor_name='Class') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/equal_split.html","id":null,"dir":"Reference","previous_headings":"","what":"Equal group sized sampling — equal_split","title":"Equal group sized sampling — equal_split","text":"Samples randomly chosen level training set equal numbers samples levels. number samples based input proportion smallest group size.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/equal_split.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Equal group sized sampling — equal_split","text":"","code":"equal_split(p_train = 1, factor_name, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/equal_split.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Equal group sized sampling — equal_split","text":"p_train (numeric) proportion samples selected training set. default 1. factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/equal_split.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Equal group sized sampling — equal_split","text":" equal_split object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/equal_split.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Equal group sized sampling — equal_split","text":"equal_split object inherits following struct classes: [equal_split] >> [split_data] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/equal_split.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Equal group sized sampling — equal_split","text":"","code":"M = equal_split(       factor_name = \"V1\",       p_train = 0.75)  D = iris_DatasetExperiment() M = equal_split(factor_name='Species') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_boxplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Feature boxplot — feature_boxplot","title":"Feature boxplot — feature_boxplot","text":"boxplot visualise distribution values within feature.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_boxplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Feature boxplot — feature_boxplot","text":"","code":"feature_boxplot(   label_outliers = TRUE,   feature_to_plot,   factor_name,   show_counts = TRUE,   style = \"boxplot\",   jitter = FALSE,   fill = FALSE,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_boxplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Feature boxplot — feature_boxplot","text":"label_outliers (logical) Label outliers. Allowed values limited following: \"TRUE\": index outlier samples included plot. \"FALSE\": labels displayed. default TRUE. feature_to_plot (character, numeric, integer) column name plotted feature. factor_name (character) name sample-meta column use. show_counts (logical) Show counts. Allowed values limited following: \"TRUE\": number samples box displayed. \"FALSE\": number samples box displayed. default TRUE. style (character) Plot style. Allowed values limited following: \"boxplot\": Boxplot style. \"violin\": Violon plot style. default \"boxplot\". jitter (logical) Include points plotted added jitter. default FALSE. fill (logical) Block fill boxes violins group colour. default FALSE. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_boxplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Feature boxplot — feature_boxplot","text":"  feature_boxplot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_boxplot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Feature boxplot — feature_boxplot","text":"feature_boxplot object inherits following struct classes: [feature_boxplot] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_boxplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Feature boxplot — feature_boxplot","text":"","code":"M = feature_boxplot(       label_outliers = FALSE,       feature_to_plot = \"V1\",       factor_name = \"V1\",       show_counts = FALSE,       style = \"boxplot\",       jitter = FALSE,       fill = FALSE)  D = MTBLS79_DatasetExperiment C = feature_boxplot(factor_name='Species',feature_to_plot='Petal.Width') chart_plot(C,D) #> Warning: no chart defined for \"function\" #> A \"feature_boxplot\" object #> -------------------------- #> name:          Feature boxplot #> description:   A boxplot to visualise the distribution of values within a feature. #> input params:  label_outliers, feature_to_plot, factor_name, show_counts, style, jitter, fill"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_profile.html","id":null,"dir":"Reference","previous_headings":"","what":"Feature profile — feature_profile","title":"Feature profile — feature_profile","text":"plot visualising change intensity feature continuous variable time, dose, run order.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_profile.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Feature profile — feature_profile","text":"","code":"feature_profile(   run_order,   qc_label,   qc_column,   colour_by,   feature_to_plot,   plot_sd = FALSE,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_profile.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Feature profile — feature_profile","text":"run_order (character) sample-meta column name containing run order. qc_label (character) label used identify QC samples. qc_column (character) sample-meta column name containing labels used identify QC samples. colour_by (character) sample-meta column name used colour plot. feature_to_plot (numeric, character, integer) name column id plotted feature. plot_sd (logical) Plot standard deviation. Allowed values limited following: \"TRUE\": Standard deviation samples QCs included plot. \"FALSE\": Standard deviation plotted. default FALSE. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_profile.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Feature profile — feature_profile","text":"  feature_profile  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_profile.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Feature profile — feature_profile","text":"feature_profile object inherits following struct classes: [feature_profile] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_profile.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Feature profile — feature_profile","text":"","code":"M = feature_profile(       run_order = character(0),       qc_label = character(0),       qc_column = character(0),       colour_by = character(0),       feature_to_plot = numeric(0),       plot_sd = FALSE)  D = MTBLS79_DatasetExperiment() C = feature_profile(run_order='run_order',     qc_label='QC',     qc_column='Class',     colour_by='Class',     feature_to_plot=1) chart_plot(C,D) #> Warning: Removed 18 rows containing missing values or values outside the scale range #> (`geom_point()`)."},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_profile_array.html","id":null,"dir":"Reference","previous_headings":"","what":"Feature profile — feature_profile_array","title":"Feature profile — feature_profile_array","text":"plot visualising change intensity feature continuous variable time, dose, run order.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_profile_array.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Feature profile — feature_profile_array","text":"","code":"feature_profile_array(   run_order,   qc_label,   qc_column,   colour_by,   feature_to_plot,   nrow = 5,   log = TRUE,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_profile_array.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Feature profile — feature_profile_array","text":"run_order (character) sample-meta column name containing run order. qc_label (character) label used identify QC samples. qc_column (character) sample-meta column name containing labels used identify QC samples. colour_by (character) sample-meta column name used colour plot. feature_to_plot (numeric, character, integer) name column id plotted feature. nrow (numeric, integer) number rows plot. default 5. log (logical) Log transform. Allowed values limited following: \"TRUE\": data log tranformed plotting. \"FALSE\": data transformed plotting. default TRUE. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_profile_array.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Feature profile — feature_profile_array","text":"  feature_profile_array  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_profile_array.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Feature profile — feature_profile_array","text":"feature_profile_array object inherits following struct classes: [feature_profile_array] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/feature_profile_array.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Feature profile — feature_profile_array","text":"","code":"M = feature_profile_array(       run_order = character(0),       qc_label = character(0),       qc_column = character(0),       colour_by = character(0),       feature_to_plot = numeric(0),       nrow = 1,       log = FALSE)  D = MTBLS79_DatasetExperiment() C = feature_profile_array(     run_order='run_order',     qc_label='QC',     qc_column='Class',     colour_by='Class',     feature_to_plot=1:3,     nrow=1,     log=TRUE) chart_plot(C,D) #> Warning: Removed 23 rows containing missing values or values outside the scale range #> (`geom_point()`)."},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_by_name.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter by name — filter_by_name","title":"Filter by name — filter_by_name","text":"Filter samples/variables row/column name, index logicals.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_by_name.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter by name — filter_by_name","text":"","code":"filter_by_name(mode = \"exclude\", dimension = \"sample\", names, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_by_name.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter by name — filter_by_name","text":"mode (character) filtering mode controls whether samples/features mode=\"included\" mode=\"excluded\" based name. default \"exclude\". dimension (character) filtering dimensions controls whether dimension=\"sample\" dimension=\"variable\" filtered based name. default \"sample\". names (character, numeric, logical) name features/samples filtered. Must exact match. Can also provide indexes (numeric) logical. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_by_name.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter by name — filter_by_name","text":" filter_by_name object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_by_name.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Filter by name — filter_by_name","text":"filter_by_name object inherits following struct classes: [filter_by_name] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_by_name.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Filter by name — filter_by_name","text":"","code":"M = filter_by_name(       mode = \"exclude\",       dimension = \"sample\",       names = character(0))  D = MTBLS79_DatasetExperiment() M = filter_by_name(mode='exclude',dimension='variable',names=c(1,2,3)) M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_na_count.html","id":null,"dir":"Reference","previous_headings":"","what":"Minimum number of measured values filter — filter_na_count","title":"Minimum number of measured values filter — filter_na_count","text":"number measured values counted feature, feature less predefined minimum number values group removed. several factors, threshold applied minimum number samples present combinations (interactions) groups.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_na_count.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Minimum number of measured values filter — filter_na_count","text":"","code":"filter_na_count(threshold, factor_name, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_na_count.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Minimum number of measured values filter — filter_na_count","text":"threshold (numeric) minimum number samples group/interaction. factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_na_count.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Minimum number of measured values filter — filter_na_count","text":" filter_na_count object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_na_count.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Minimum number of measured values filter — filter_na_count","text":"filter_na_count object inherits following struct classes: [filter_na_count] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_na_count.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Minimum number of measured values filter — filter_na_count","text":"","code":"M = filter_na_count(       threshold = 2,       factor_name = \"V1\")  D = MTBLS79_DatasetExperiment() M = filter_na_count(threshold=3,factor_name='Class') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_smeta.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter by sample meta data — filter_smeta","title":"Filter by sample meta data — filter_smeta","text":"data filtered named levels factor included/excluded dataset.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_smeta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter by sample meta data — filter_smeta","text":"","code":"filter_smeta(mode = \"include\", levels, factor_name, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_smeta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter by sample meta data — filter_smeta","text":"mode (character) Mode action. Allowed values limited following: \"include\": Samples specified levels retained. \"exclude\": Samples specified levels excluded. default \"include\". levels (character) level name(s) filtering. factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_smeta.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter by sample meta data — filter_smeta","text":" filter_smeta object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_smeta.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Filter by sample meta data — filter_smeta","text":"filter_smeta object inherits following struct classes: [filter_smeta] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/filter_smeta.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Filter by sample meta data — filter_smeta","text":"","code":"M = filter_smeta(       mode = \"include\",       levels = character(0),       factor_name = \"V1\")  D = MTBLS79_DatasetExperiment() M = filter_smeta(mode='exclude',levels='QC',factor_name='QC') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fisher_exact.html","id":null,"dir":"Reference","previous_headings":"","what":"Fisher Exact Test — fisher_exact","title":"Fisher Exact Test — fisher_exact","text":"fisher exact test used analyse contingency tables comparing number correctly/incorrectly predicted group labels. multiple test corrected p-value indicates whether number measured values significantly different groups.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fisher_exact.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fisher Exact Test — fisher_exact","text":"","code":"fisher_exact(alpha = 0.05, mtc = \"fdr\", factor_name, factor_pred, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fisher_exact.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fisher Exact Test — fisher_exact","text":"alpha (numeric) p-value cutoff determining significance. default 0.05. mtc (character) Multiple test correction method. Allowed values limited following: \"bonferroni\": Bonferroni correction p-values multiplied number comparisons. \"fdr\": Benjamini Hochberg False Discovery Rate correction. \"none\": correction. default \"fdr\". factor_name (character) name sample-meta column use. factor_pred (data.frame) data.frame, column factor predicted group labels compare true groups labels. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fisher_exact.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fisher Exact Test — fisher_exact","text":" fisher_exact object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fisher_exact.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Fisher Exact Test — fisher_exact","text":"fisher_exact object inherits following struct classes: [fisher_exact] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fisher_exact.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fisher Exact Test — fisher_exact","text":"","code":"M = fisher_exact(       alpha = 0.05,       mtc = \"fdr\",       factor_name = \"V1\",       factor_pred = data.frame(id=NA))  # load some data D=MTBLS79_DatasetExperiment()  # prepare predictions based on NA pred=as.data.frame(is.na(D$data)) pred=lapply(pred,factor,levels=c(TRUE,FALSE)) pred=as.data.frame(pred)  # apply method M = fisher_exact(alpha=0.05,mtc='fdr',factor_name='Class',factor_pred=pred) M=model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change.html","id":null,"dir":"Reference","previous_headings":"","what":"Fold change — fold_change","title":"Fold change — fold_change","text":"Fold change relative change mean (non-parametric equivalent) intensities feature pairs levels factor.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fold change — fold_change","text":"","code":"fold_change(   factor_name,   paired = FALSE,   sample_name = character(0),   threshold = 2,   control_group = character(0),   method = \"geometric\",   conf_level = 0.95,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fold change — fold_change","text":"factor_name (character) name sample-meta column use. paired (logical) Paired fold change. Allowed values limited following: \"TRUE\": Fold change calculated taking account paired sampling. \"FALSE\": Fold change calculated assuming paired sampling. default FALSE. sample_name (character) name sample_meta column containing sample identifiers paired sampling. default character(0). threshold (numeric) fold change threshold labelling features significant. default 2. control_group (character) level name group used denominator (possible) computing fold change. default character(0). method (character) Fold change method. Allowed values limited following: \"geometric\": log transform applied using group means calculate fold change. non-tranformedspace equivalent using geometric group means. Confidence intervals independant paired sampling estimated using standard error mean log transformed space transformed back original space. \"median\": group medians method described Price Bonett used estimate confidence intervals. paired data standard error median used estimate confidence intervals median fold change pairs. \"mean\": group means method described Price Bonnet used estimate confidence intervals. paired data standard error mean used estimate confidence intervals mean fold change pairs. default \"geometric\". conf_level (numeric) confidence level interval. default 0.95. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fold change — fold_change","text":" fold_change object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Fold change — fold_change","text":"fold_change object inherits following struct classes: [fold_change] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fold change — fold_change","text":"Price Jr RM, Bonett DG (2020). \"Confidence Intervals Ratios Means Medians.\" Journal Educational Behavioral Statistics, 45(6), 750-770.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fold change — fold_change","text":"","code":"M = fold_change(       factor_name = \"V1\",       sample_name = character(0),       paired = FALSE,       threshold = 2,       control_group = character(0),       method = \"geometric\",       conf_level = 0.95)  D = MTBLS79_DatasetExperiment() M = fold_change(factor_name='Class') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change_int.html","id":null,"dir":"Reference","previous_headings":"","what":"Fold change for interactions between factors — fold_change_int","title":"Fold change for interactions between factors — fold_change_int","text":"one factor fold change calculation extended include combinations levels (interactions) factors. Paired fold changes possible computation.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change_int.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fold change for interactions between factors — fold_change_int","text":"","code":"fold_change_int(   factor_name,   threshold = 2,   control_group = character(0),   method = \"geometric\",   conf_level = 0.95,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change_int.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fold change for interactions between factors — fold_change_int","text":"factor_name (character) name sample-meta column use. threshold (numeric) fold change threshold labelling features significant. default 2. control_group (character) level names groups used denominator (possible) computing fold change. One level factor, assumed order factor_name. default character(0). method (character) Fold change method. Allowed values limited following: \"geometric\": log transform applied using group means calculate fold change. non-tranformedspace equivalent using geometric group means. Confidence intervals independant paired sampling estimated using standard error mean log transformed space transformed back original space. \"median\": group medians method described Price Bonett used estimate confidence intervals. paired data standard error median used estimate confidence intervals median fold change pairs. \"mean\": group means method described Price Bonnet used estimate confidence intervals. paired data standard error mean used estimate confidence intervals mean fold change pairs. default \"geometric\". conf_level (numeric) confidence level interval. default 0.95. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change_int.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fold change for interactions between factors — fold_change_int","text":" fold_change_int object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change_int.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Fold change for interactions between factors — fold_change_int","text":"fold_change_int object inherits following struct classes: [fold_change_int] >> [fold_change] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change_int.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fold change for interactions between factors — fold_change_int","text":"Lloyd GR, Jankevics , Weber RJM (2020). \"struct: R/Bioconductor-based framework standardized metabolomics data analysis beyond.\" Bioinformatics, 36(22-23), 5551-5552. https://doi.org/10.1093/bioinformatics/btaa1031.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change_int.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fold change for interactions between factors — fold_change_int","text":"","code":"M = fold_change_int(       factor_name = \"V1\",       sample_name = character(0),       threshold = 2,       control_group = character(0),       method = \"geometric\",       paired = FALSE,       conf_level = 0.95)  D = MTBLS79_DatasetExperiment() D=D[,1:10,drop=FALSE] M = filter_smeta(mode='exclude',levels='QC',factor_name='Class') +     fold_change_int(factor_name=c('Class','Batch')) M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Fold change plot — fold_change_plot","title":"Fold change plot — fold_change_plot","text":"plot fold changes calculated chosen subset features. predefined fold change threshold indicated shaded regions.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fold change plot — fold_change_plot","text":"","code":"fold_change_plot(number_features = 20, orientation = \"portrait\", ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fold change plot — fold_change_plot","text":"number_features (numeric) number randomly selected features plot, list column numbers. default 20. orientation (character) Plot orientation. Allowed values limited following: \"landscape\": Features plotted y-axis. \"portrait\": Features plotted x-axis. default \"portrait\". ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fold change plot — fold_change_plot","text":"  fold_change_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change_plot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Fold change plot — fold_change_plot","text":"fold_change_plot object inherits following struct classes: [fold_change_plot] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fold_change_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fold change plot — fold_change_plot","text":"","code":"M = fold_change_plot(       number_features = 10,       orientation = \"portrait\")  C = fold_change_plot()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/forward_selection_by_rank.html","id":null,"dir":"Reference","previous_headings":"","what":"Forward selection by rank — forward_selection_by_rank","title":"Forward selection by rank — forward_selection_by_rank","text":"model trained performance metric computed including increasing numbers features model. features included step defined rank, computed another variable e.g. VIP score. \"optimal\"subset features suggested minimising input performance metric.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/forward_selection_by_rank.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Forward selection by rank — forward_selection_by_rank","text":"","code":"forward_selection_by_rank(   min_no_vars = 1,   max_no_vars = 100,   step_size = 1,   factor_name,   variable_rank,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/forward_selection_by_rank.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Forward selection by rank — forward_selection_by_rank","text":"min_no_vars (numeric) minimum number variables include model. default 1. max_no_vars (numeric) maximum number variables include model. default 100. step_size (numeric) incremental change number features model. default 1. factor_name (character) name sample-meta column use. variable_rank (numeric, integer) values used rank features. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/forward_selection_by_rank.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Forward selection by rank — forward_selection_by_rank","text":" forward_selection_by_rank object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/forward_selection_by_rank.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Forward selection by rank — forward_selection_by_rank","text":"forward_selection_by_rank object inherits following struct classes: [forward_selection_by_rank] >> [resampler] >> [iterator] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/forward_selection_by_rank.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Forward selection by rank — forward_selection_by_rank","text":"","code":"M = forward_selection_by_rank(       min_no_vars = 1,       max_no_vars = 100,       step_size = 1,       factor_name = \"V1\",       variable_rank = 1)  # some data D = MTBLS79_DatasetExperiment(filtered=TRUE)  # normalise, impute and scale then remove QCs P = pqn_norm(qc_label='QC',factor_name='Class') +     knn_impute(neighbours=5) +     glog_transform(qc_label='QC',factor_name='Class') +     filter_smeta(mode='exclude',levels='QC',factor_name='Class') P = model_apply(P,D) D = predicted(P)  # forward selection using a PLSDA model M = forward_selection_by_rank(factor_name='Class',                              min_no_vars=2,                              max_no_vars=11,                              variable_rank=1:2063) *     (mean_centre() + PLSDA(number_components=1,                            factor_name='Class')) M = run(M,D,balanced_accuracy())"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fs_line.html","id":null,"dir":"Reference","previous_headings":"","what":"Forward selection line plot — fs_line","title":"Forward selection line plot — fs_line","text":"line plot forward selection. computed model performance metric plotted number features included model.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fs_line.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Forward selection line plot — fs_line","text":"","code":"fs_line(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fs_line.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Forward selection line plot — fs_line","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fs_line.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Forward selection line plot — fs_line","text":"  fs_line  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fs_line.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Forward selection line plot — fs_line","text":"fs_line object inherits following struct classes: [fs_line] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/fs_line.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Forward selection line plot — fs_line","text":"","code":"M = fs_line()  # some data D = MTBLS79_DatasetExperiment(filtered=TRUE)  # normalise, impute and scale then remove QCs P = pqn_norm(qc_label='QC',factor_name='Class') +     knn_impute(neighbours=5) +     glog_transform(qc_label='QC',factor_name='Class') +     filter_smeta(mode='exclude',levels='QC',factor_name='Class') P = model_apply(P,D) D = predicted(P)  # forward selection using a PLSDA model M = forward_selection_by_rank(factor_name='Class',                              min_no_vars=2,                              max_no_vars=11,                              variable_rank=1:2063) *     (mean_centre() + PLSDA(number_components=1,                            factor_name='Class')) M = run(M,D,balanced_accuracy())  # chart C = fs_line() chart_plot(C,M)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_opt_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Glog optimisation — glog_opt_plot","title":"Glog optimisation — glog_opt_plot","text":"plot sum squares error (SSE) vs different values lambda glog transform. indicated optimum value lambda minimises SSE.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_opt_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Glog optimisation — glog_opt_plot","text":"","code":"glog_opt_plot(plot_grid = 100, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_opt_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Glog optimisation — glog_opt_plot","text":"plot_grid (numeric)  default 100. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_opt_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Glog optimisation — glog_opt_plot","text":"  glog_opt_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_opt_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Glog optimisation — glog_opt_plot","text":"object makes use functionality following packages: pmp","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_opt_plot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Glog optimisation — glog_opt_plot","text":"glog_opt_plot object inherits following struct classes: [glog_opt_plot] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_opt_plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Glog optimisation — glog_opt_plot","text":"Jankevics , Lloyd GR, Weber RJM (????). pmp: Peak Matrix Processing signal batch correction metabolomics datasets. R package version 1.15.1.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_opt_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Glog optimisation — glog_opt_plot","text":"","code":"M = glog_opt_plot(       plot_grid = numeric(0))  D = iris_DatasetExperiment() M = glog_transform(qc_label='versicolor',factor_name='Species') M = model_apply(M,D) #> Error!Lambda tending to infinity! Using standard #> Error!Lambda tending to infinity! Using standard C = glog_opt_plot() chart_plot(C,M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_transform.html","id":null,"dir":"Reference","previous_headings":"","what":"Generalised logarithmic transform — glog_transform","title":"Generalised logarithmic transform — glog_transform","text":"generalised logarithm (glog) transformation applies log transformation applying offset account technical variation.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_transform.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generalised logarithmic transform — glog_transform","text":"","code":"glog_transform(qc_label = \"QC\", factor_name, lambda = NULL, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_transform.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generalised logarithmic transform — glog_transform","text":"qc_label (character) label used identify QC samples. default \"QC\". factor_name (character) name sample-meta column use. lambda (numeric, NULL) value lambda use. NULL pmp package used determine \"optimal\" value lambda. default NULL. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_transform.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generalised logarithmic transform — glog_transform","text":" glog_transform object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_transform.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generalised logarithmic transform — glog_transform","text":"object makes use functionality following packages: pmp","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_transform.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Generalised logarithmic transform — glog_transform","text":"glog_transform object inherits following struct classes: [glog_transform] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_transform.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Generalised logarithmic transform — glog_transform","text":"Jankevics , Lloyd GR, Weber RJM (????). pmp: Peak Matrix Processing signal batch correction metabolomics datasets. R package version 1.15.1. Durbin B, Hardin J, Hawkins D, Rocke D (2002). \"variance-stabilizing transformation gene-expression microarray data.\" Bioinformatics, 18(Suppl 1), S105-S110. Parsons HM, Ludwig C, Gunther UL, Viant MR (2007). \"Improved classification accuracy 1- ', '2-dimensional NMR metabolomics data using variance ', 'stabilising generalised logarithm transformation.\" Bioinformatics, 8(1), 234.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/glog_transform.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generalised logarithmic transform — glog_transform","text":"","code":"M = glog_transform(       qc_label = \"QC\",       factor_name = \"V1\",       lambda = NULL)  D = iris_DatasetExperiment() M = glog_transform(qc_label='versicolor',factor_name='Species') M = model_apply(M,D) #> Error!Lambda tending to infinity! Using standard #> Error!Lambda tending to infinity! Using standard"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/grid_search_1d.html","id":null,"dir":"Reference","previous_headings":"","what":"One dimensional grid search — grid_search_1d","title":"One dimensional grid search — grid_search_1d","text":"one dimensional grid search calculates performance metric model evenly spaced values model input parameter. \"optimum\" value parameter suggested one maximises performance, minimises error (whichever appropriate chosen metric)","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/grid_search_1d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"One dimensional grid search — grid_search_1d","text":"","code":"grid_search_1d(   param_to_optimise,   search_values,   model_index,   factor_name,   max_min = \"min\",   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/grid_search_1d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"One dimensional grid search — grid_search_1d","text":"param_to_optimise (character) name model input parameter focus search. search_values () values input parameter optimised. model_index (numeric, integer) index model sequence uses parameter optimised. factor_name (character) name sample-meta column use. max_min (character) Maximise minimise. Allowed values limited following: \"max\": optimium parameter value suggested based maximising performance metric. \"min\": optimium parameter value suggested based minimising performance metric. default \"min\". ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/grid_search_1d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"One dimensional grid search — grid_search_1d","text":" grid_search_1d object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/grid_search_1d.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"One dimensional grid search — grid_search_1d","text":"grid_search_1d object inherits following struct classes: [grid_search_1d] >> [resampler] >> [iterator] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/grid_search_1d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"One dimensional grid search — grid_search_1d","text":"","code":"M = grid_search_1d(       param_to_optimise = character(0),       search_values = numeric(0),       model_index = numeric(0),       factor_name = \"V1\",       max_min = \"min\")  D = MTBLS79_DatasetExperiment() # some preprocessing M = pqn_norm(qc_label='QC',factor_name='Class') +     knn_impute() +     glog_transform(qc_label='QC',factor_name='Class') +     filter_smeta(factor_name='Class',levels='QC',mode='exclude') M=model_apply(M,D) D=predicted(M)  # reduce number of features for this example D=D[,1:10]  # optmise number of components for PLS model I = grid_search_1d(param_to_optimise='number_components',search_values=1:5,         model_index=2,factor_name='Class') *         (mean_centre()+PLSDA(factor_name='Class')) I = run(I,D,balanced_accuracy())"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/gs_line.html","id":null,"dir":"Reference","previous_headings":"","what":"Grid search line plot — gs_line","title":"Grid search line plot — gs_line","text":"plot calculated performance metric model input parameter values used train model. optimum parameter value indicated based minimising (maximising) chosen metric.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/gs_line.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Grid search line plot — gs_line","text":"","code":"gs_line(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/gs_line.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Grid search line plot — gs_line","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/gs_line.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Grid search line plot — gs_line","text":"  gs_line  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/gs_line.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Grid search line plot — gs_line","text":"gs_line object inherits following struct classes: [gs_line] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/gs_line.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Grid search line plot — gs_line","text":"","code":"M = gs_line()  C = gs_line()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/hca_dendrogram.html","id":null,"dir":"Reference","previous_headings":"","what":"HCA dendrogram — hca_dendrogram","title":"HCA dendrogram — hca_dendrogram","text":"dendrogram visualising clustering HCA.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/hca_dendrogram.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"HCA dendrogram — hca_dendrogram","text":"","code":"hca_dendrogram(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/hca_dendrogram.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"HCA dendrogram — hca_dendrogram","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/hca_dendrogram.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"HCA dendrogram — hca_dendrogram","text":"  hca_dendrogram  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/hca_dendrogram.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"HCA dendrogram — hca_dendrogram","text":"object makes use functionality following packages: ggdendro","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/hca_dendrogram.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"HCA dendrogram — hca_dendrogram","text":"hca_dendrogram object inherits following struct classes: [hca_dendrogram] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/hca_dendrogram.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"HCA dendrogram — hca_dendrogram","text":"de Vries , Ripley BD (2024). ggdendro: Create Dendrograms Tree Diagrams Using 'ggplot2'. R package version 0.2.0, https://CRAN.R-project.org/package=ggdendro.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/hca_dendrogram.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"HCA dendrogram — hca_dendrogram","text":"","code":"M = hca_dendrogram()  C = hca_dendrogram()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfold_xval.html","id":null,"dir":"Reference","previous_headings":"","what":"k-fold cross-validation — kfold_xval","title":"k-fold cross-validation — kfold_xval","text":"k-fold cross-validation iterative approach applied validate models. samples divided k \"folds\", subsets. subset excluded model training used model validation , resulting single left-prediction sample. Model performance metrics computed training test sets across folds.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfold_xval.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"k-fold cross-validation — kfold_xval","text":"","code":"kfold_xval(folds = 10, method = \"venetian\", factor_name, collect = NULL, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfold_xval.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"k-fold cross-validation — kfold_xval","text":"folds (numeric, integer) number cross-validation folds. default 10. method (character) Fold selection method. Allowed values limited following: \"venetian\": Every nth sample assigned fold, n number folds. \"blocks\": Blocks adjacent samples assigned fold. \"random\": Samples randomly assigned fold. default \"venetian\". factor_name (character) name sample-meta column use. collect (NULL, character) name model output collect bootstrap repetitions, addition input metric. default NULL. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfold_xval.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"k-fold cross-validation — kfold_xval","text":" kfold_xval object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfold_xval.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"k-fold cross-validation — kfold_xval","text":"kfold_xval object inherits following struct classes: [kfold_xval] >> [resampler] >> [iterator] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfold_xval.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"k-fold cross-validation — kfold_xval","text":"","code":"M = kfold_xval(       folds = 5,       method = \"random\",       factor_name = \"V1\",       collect = NULL)  D = iris_DatasetExperiment() I = kfold_xval(factor_name='Species') *     (mean_centre() + PLSDA(factor_name='Species')) I = run(I,D,balanced_accuracy())"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfoldxcv_grid.html","id":null,"dir":"Reference","previous_headings":"","what":"k-fold cross validation plot — kfoldxcv_grid","title":"k-fold cross validation plot — kfoldxcv_grid","text":"graphic visualising true class predicted class samples groups cross-validation folds.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfoldxcv_grid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"k-fold cross validation plot — kfoldxcv_grid","text":"","code":"kfoldxcv_grid(factor_name, level, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfoldxcv_grid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"k-fold cross validation plot — kfoldxcv_grid","text":"factor_name (character) name sample-meta column use. level (character) level/group plot. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfoldxcv_grid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"k-fold cross validation plot — kfoldxcv_grid","text":"  kfoldxcv_grid  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfoldxcv_grid.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"k-fold cross validation plot — kfoldxcv_grid","text":"kfoldxcv_grid object inherits following struct classes: [kfoldxcv_grid] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfoldxcv_grid.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"k-fold cross validation plot — kfoldxcv_grid","text":"","code":"M = kfoldxcv_grid(       factor_name = \"V1\",       level = \"level_1\")  D = iris_DatasetExperiment() I = kfold_xval(factor_name='Species') *     (mean_centre() + PLSDA(factor_name='Species')) I = run(I,D,balanced_accuracy())  C = kfoldxcv_grid(factor_name='Species',level='setosa') chart_plot(C,I) #> Warning: Removed 900 rows containing missing values or values outside the scale range #> (`geom_tile()`)."},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfoldxcv_metric.html","id":null,"dir":"Reference","previous_headings":"","what":"kfoldxcv metric plot — kfoldxcv_metric","title":"kfoldxcv metric plot — kfoldxcv_metric","text":"boxplot performance metric computed fold k-fold cross-validation.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfoldxcv_metric.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"kfoldxcv metric plot — kfoldxcv_metric","text":"","code":"kfoldxcv_metric(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfoldxcv_metric.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"kfoldxcv metric plot — kfoldxcv_metric","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfoldxcv_metric.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"kfoldxcv metric plot — kfoldxcv_metric","text":"  kfoldxcv_metric  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfoldxcv_metric.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"kfoldxcv metric plot — kfoldxcv_metric","text":"kfoldxcv_metric object inherits following struct classes: [kfoldxcv_metric] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kfoldxcv_metric.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"kfoldxcv metric plot — kfoldxcv_metric","text":"","code":"M = kfoldxcv_metric()  C = kfoldxcv_metric()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/knn_impute.html","id":null,"dir":"Reference","previous_headings":"","what":"kNN missing value imputation — knn_impute","title":"kNN missing value imputation — knn_impute","text":"k-nearest neighbour missing value imputation replaces missing values data average predefined number similar neighbours value present","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/knn_impute.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"kNN missing value imputation — knn_impute","text":"","code":"knn_impute(   neighbours = 5,   sample_max = 50,   feature_max = 50,   by = \"features\",   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/knn_impute.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"kNN missing value imputation — knn_impute","text":"neighbours (numeric) number neighbours (k) use imputation. default 5. sample_max (numeric) maximum percent missing values per sample. default 50. feature_max (numeric) maximum percent missing values per feature. default 50. (character) Impute using similar \"samples\" \"features\". Default features. default \"features\". ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/knn_impute.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"kNN missing value imputation — knn_impute","text":" knn_impute object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/knn_impute.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"kNN missing value imputation — knn_impute","text":"object makes use functionality following packages: pmp","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/knn_impute.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"kNN missing value imputation — knn_impute","text":"knn_impute object inherits following struct classes: [knn_impute] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/knn_impute.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"kNN missing value imputation — knn_impute","text":"Jankevics , Lloyd GR, Weber RJM (????). pmp: Peak Matrix Processing signal batch correction metabolomics datasets. R package version 1.15.1.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/knn_impute.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"kNN missing value imputation — knn_impute","text":"","code":"M = knn_impute(       neighbours = 5,       feature_max = 50,       sample_max = 50,       by = \"features\")  M = knn_impute()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kw_p_hist.html","id":null,"dir":"Reference","previous_headings":"","what":"Histogram of p values — kw_p_hist","title":"Histogram of p values — kw_p_hist","text":"histogram p-values computed kruskal-wallis method","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kw_p_hist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Histogram of p values — kw_p_hist","text":"","code":"kw_p_hist(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kw_p_hist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Histogram of p values — kw_p_hist","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kw_p_hist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Histogram of p values — kw_p_hist","text":"  kw_p_hist  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kw_p_hist.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Histogram of p values — kw_p_hist","text":"kw_p_hist object inherits following struct classes: [kw_p_hist] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kw_p_hist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Histogram of p values — kw_p_hist","text":"","code":"M = kw_p_hist()  C = kw_p_hist()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kw_rank_sum.html","id":null,"dir":"Reference","previous_headings":"","what":"Kruskal-Wallis rank sum test — kw_rank_sum","title":"Kruskal-Wallis rank sum test — kw_rank_sum","text":"Kruskal-Wallis test univariate hypothesis testing method allows multiple (n>=2) groups compared without making assumption values normally distributed. non-parametric equivalent 1-way ANOVA. test applied variables/features individually, multiple test corrected p-values computed indicate significance variables/features.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kw_rank_sum.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Kruskal-Wallis rank sum test — kw_rank_sum","text":"","code":"kw_rank_sum(alpha = 0.05, mtc = \"fdr\", factor_names, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kw_rank_sum.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Kruskal-Wallis rank sum test — kw_rank_sum","text":"alpha (numeric) p-value cutoff determining significance. default 0.05. mtc (character) Multiple test correction method. Allowed values limited following: \"bonferroni\": Bonferroni correction p-values multiplied number comparisons. \"fdr\": Benjamini Hochberg False Discovery Rate correction. \"none\": correction. default \"fdr\". factor_names (character) name sample meta column(s) use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kw_rank_sum.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Kruskal-Wallis rank sum test — kw_rank_sum","text":" kw_rank_sum object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kw_rank_sum.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Kruskal-Wallis rank sum test — kw_rank_sum","text":"kw_rank_sum object inherits following struct classes: [kw_rank_sum] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/kw_rank_sum.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Kruskal-Wallis rank sum test — kw_rank_sum","text":"","code":"M = kw_rank_sum(       alpha = 0.05,       mtc = \"fdr\",       factor_names = \"V1\")  D = iris_DatasetExperiment() M = kw_rank_sum(factor_names='Species') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/linear_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Linear model — linear_model","title":"Linear model — linear_model","text":"Linear models can used carry regression, single stratum analysis variance analysis covariance.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/linear_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Linear model — linear_model","text":"","code":"linear_model(formula, na_action = \"na.omit\", contrasts = list(), ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/linear_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Linear model — linear_model","text":"formula (formula) symbolic description model fitted. na_action (character) NA action. Allowed values limited following: \"na.omit\": Incomplete cases removed. \"na.fail\": error thrown NA present. \"na.exclude\": Incomplete cases removed, output result padded correct size using NA. \"na.pass\": apply linear model NA present. default \"na.omit\". contrasts (list) contrasts associated factor. default list(). ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/linear_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Linear model — linear_model","text":" linear_model object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/linear_model.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Linear model — linear_model","text":"object makes use functionality following packages: stats","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/linear_model.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Linear model — linear_model","text":"linear_model object inherits following struct classes: [linear_model] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/linear_model.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Linear model — linear_model","text":"R Core Team (2024). R: Language Environment Statistical Computing. R Foundation Statistical Computing, Vienna, Austria. https://www.R-project.org/.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/linear_model.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Linear model — linear_model","text":"","code":"M = linear_model(       formula = y ~ x,       na_action = \"na.omit\",       contrasts = list())  D = iris_DatasetExperiment() M = linear_model(formula = y~Species)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/log_transform.html","id":null,"dir":"Reference","previous_headings":"","what":"logarithm transform — log_transform","title":"logarithm transform — log_transform","text":"logarithmic transform applied values data matrix.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/log_transform.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"logarithm transform — log_transform","text":"","code":"log_transform(base = 10, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/log_transform.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"logarithm transform — log_transform","text":"base (numeric) base logarithm used transform. default 10. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/log_transform.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"logarithm transform — log_transform","text":" log_transform object following output slots: struct object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/log_transform.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"logarithm transform — log_transform","text":"log_transform object inherits following struct classes: [log_transform] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/log_transform.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"logarithm transform — log_transform","text":"","code":"M = log_transform(       base = 10)  M = log_transform()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mean_centre.html","id":null,"dir":"Reference","previous_headings":"","what":"Mean centre — mean_centre","title":"Mean centre — mean_centre","text":"mean sample subtracted samples data matrix. features centred matrix zero mean.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mean_centre.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mean centre — mean_centre","text":"","code":"mean_centre(mode = \"data\", ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mean_centre.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mean centre — mean_centre","text":"mode (character) Mode action. Allowed values limited following: \"data\": Centring applied data block. \"sample_meta\": Centring applied sample_meta block. \"\": Centring applied data sample_meta blocks. default \"data\". ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mean_centre.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Mean centre — mean_centre","text":" mean_centre object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mean_centre.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Mean centre — mean_centre","text":"mean_centre object inherits following struct classes: [mean_centre] >> [preprocess] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mean_centre.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mean centre — mean_centre","text":"","code":"M = mean_centre(       mode = \"data\")  M = mean_centre()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mean_of_medians.html","id":null,"dir":"Reference","previous_headings":"","what":"Mean of medians — mean_of_medians","title":"Mean of medians — mean_of_medians","text":"data matrix normalised mean median factor level.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mean_of_medians.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mean of medians — mean_of_medians","text":"","code":"mean_of_medians(factor_name, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mean_of_medians.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mean of medians — mean_of_medians","text":"factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mean_of_medians.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Mean of medians — mean_of_medians","text":" mean_of_medians object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mean_of_medians.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Mean of medians — mean_of_medians","text":"mean_of_medians object inherits following struct classes: [mean_of_medians] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mean_of_medians.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mean of medians — mean_of_medians","text":"","code":"M = mean_of_medians(       factor_name = \"V1\")  D = iris_DatasetExperiment() M = mean_of_medians(factor_name='Species') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mixed_effect.html","id":null,"dir":"Reference","previous_headings":"","what":"Mixed effects model — mixed_effect","title":"Mixed effects model — mixed_effect","text":"mixed effects model extension ANOVA fixed random effects.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mixed_effect.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mixed effects model — mixed_effect","text":"","code":"mixed_effect(alpha = 0.05, mtc = \"fdr\", formula, ss_type = \"marginal\", ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mixed_effect.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mixed effects model — mixed_effect","text":"alpha (numeric) p-value cutoff determining significance. default 0.05. mtc (character) Multiple test correction method. Allowed values limited following: \"bonferroni\": Bonferroni correction p-values multiplied number comparisons. \"fdr\": Benjamini Hochberg False Discovery Rate correction. \"none\": correction. default \"fdr\". formula (formula) symbolic description model fitted. ss_type (character) Sum squares type. Allowed values limited following: \"marginal\": Type III sum squares. \"sequential\": Type II sum squares. default \"marginal\". ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mixed_effect.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Mixed effects model — mixed_effect","text":" mixed_effect object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mixed_effect.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Mixed effects model — mixed_effect","text":"object makes use functionality following packages: nlme emmeans","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mixed_effect.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Mixed effects model — mixed_effect","text":"mixed_effect object inherits following struct classes: [mixed_effect] >> [ANOVA] >> [model] >> [stato] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mixed_effect.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Mixed effects model — mixed_effect","text":"Pinheiro J, Bates D, R Core Team (2023). nlme: Linear Nonlinear Mixed Effects Models. R package version 3.1-164, https://CRAN.R-project.org/package=nlme. Pinheiro JC, Bates DM (2000). Mixed-Effects Models S S-PLUS. Springer, New York. doi:10.1007/b98882 https://doi.org/10.1007/b98882. Lenth R (2024). emmeans: Estimated Marginal Means, aka Least-Squares Means. R package version 1.10.1, https://CRAN.R-project.org/package=emmeans. Fox J, Weisberg S (2019). R Companion Applied Regression, Third edition. Sage, Thousand Oaks CA. https://socialsciences.mcmaster.ca/jfox/Books/Companion/.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mixed_effect.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mixed effects model — mixed_effect","text":"","code":"M = mixed_effect(       alpha = 0.05,       mtc = \"fdr\",       formula = y ~ x,       ss_type = \"marginal\")  D = iris_DatasetExperiment() D$sample_meta$id=rownames(D) # dummy id column M = mixed_effect(formula = y~Species+ Error(id/Species)) M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_apply.html","id":null,"dir":"Reference","previous_headings":"","what":"Apply method — model_apply,ANOVA,DatasetExperiment-method","title":"Apply method — model_apply,ANOVA,DatasetExperiment-method","text":"Applies method input DatasetExperiment","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_apply.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Apply method — model_apply,ANOVA,DatasetExperiment-method","text":"","code":"# S4 method for class 'ANOVA,DatasetExperiment' model_apply(M, D)  # S4 method for class 'HSD,DatasetExperiment' model_apply(M, D)  # S4 method for class 'mixed_effect,DatasetExperiment' model_apply(M, D)  # S4 method for class 'HSDEM,DatasetExperiment' model_apply(M, D)  # S4 method for class 'classical_lsq,DatasetExperiment' model_apply(M, D)  # S4 method for class 'confounders_clsq,DatasetExperiment' model_apply(M, D)  # S4 method for class 'constant_sum_norm,DatasetExperiment' model_apply(M, D)  # S4 method for class 'corr_coef,DatasetExperiment' model_apply(M, D)  # S4 method for class 'split_data,DatasetExperiment' model_apply(M, D)  # S4 method for class 'equal_split,DatasetExperiment' model_apply(M, D)  # S4 method for class 'filter_smeta,DatasetExperiment' model_apply(M, D)  # S4 method for class 'fisher_exact,DatasetExperiment' model_apply(M, D)  # S4 method for class 'fold_change,DatasetExperiment' model_apply(M, D)  # S4 method for class 'fold_change_int,DatasetExperiment' model_apply(M, D)  # S4 method for class 'HCA,DatasetExperiment' model_apply(M, D)  # S4 method for class 'knn_impute,DatasetExperiment' model_apply(M, D)  # S4 method for class 'kw_rank_sum,DatasetExperiment' model_apply(M, D)  # S4 method for class 'log_transform,DatasetExperiment' model_apply(M, D)  # S4 method for class 'mean_of_medians,DatasetExperiment' model_apply(M, D)  # S4 method for class 'nroot_transform,DatasetExperiment' model_apply(M, D)  # S4 method for class 'pairs_filter,DatasetExperiment' model_apply(M, D)  # S4 method for class 'prop_na,DatasetExperiment' model_apply(M, D)  # S4 method for class 'rsd_filter,DatasetExperiment' model_apply(M, D)  # S4 method for class 'sb_corr,DatasetExperiment' model_apply(M, D)  # S4 method for class 'stratified_split,DatasetExperiment' model_apply(M, D)  # S4 method for class 'tSNE,DatasetExperiment' model_apply(M, D)  # S4 method for class 'ttest,DatasetExperiment' model_apply(M, D)  # S4 method for class 'vec_norm,DatasetExperiment' model_apply(M, D)  # S4 method for class 'wilcox_test,DatasetExperiment' model_apply(M, D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_apply.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Apply method — model_apply,ANOVA,DatasetExperiment-method","text":"M method object D another object used first","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_apply.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Apply method — model_apply,ANOVA,DatasetExperiment-method","text":"Returns modified method object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_apply.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Apply method — model_apply,ANOVA,DatasetExperiment-method","text":"","code":"M=model() model_apply(M,DatasetExperiment()) #> Warning: no training implemented for this model #> A \"model\" object #> ---------------- #> name:           #> description:    #> predicted:      #> seq_in:        data #>"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_predict.html","id":null,"dir":"Reference","previous_headings":"","what":"Model prediction — model_predict,DFA,DatasetExperiment-method","title":"Model prediction — model_predict,DFA,DatasetExperiment-method","text":"Apply model using input DatasetExperiment. Assumes model trained first.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_predict.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Model prediction — model_predict,DFA,DatasetExperiment-method","text":"","code":"# S4 method for class 'DFA,DatasetExperiment' model_predict(M, D)  # S4 method for class 'PCA,DatasetExperiment' model_predict(M, D)  # S4 method for class 'PLSR,DatasetExperiment' model_predict(M, D)  # S4 method for class 'PLSDA,DatasetExperiment' model_predict(M, D)  # S4 method for class 'autoscale,DatasetExperiment' model_predict(M, D)  # S4 method for class 'blank_filter,DatasetExperiment' model_predict(M, D)  # S4 method for class 'constant_sum_norm,DatasetExperiment' model_predict(M, D)  # S4 method for class 'dratio_filter,DatasetExperiment' model_predict(M, D)  # S4 method for class 'filter_by_name,DatasetExperiment' model_predict(M, D)  # S4 method for class 'filter_na_count,DatasetExperiment' model_predict(M, D)  # S4 method for class 'filter_smeta,DatasetExperiment' model_predict(M, D)  # S4 method for class 'glog_transform,DatasetExperiment' model_predict(M, D)  # S4 method for class 'linear_model,DatasetExperiment' model_predict(M, D)  # S4 method for class 'mean_centre,DatasetExperiment' model_predict(M, D)  # S4 method for class 'mv_feature_filter,DatasetExperiment' model_predict(M, D)  # S4 method for class 'mv_sample_filter,DatasetExperiment' model_predict(M, D)  # S4 method for class 'OPLSR,DatasetExperiment' model_predict(M, D)  # S4 method for class 'OPLSDA,DatasetExperiment' model_predict(M, D)  # S4 method for class 'pareto_scale,DatasetExperiment' model_predict(M, D)  # S4 method for class 'pqn_norm,DatasetExperiment' model_predict(M, D)  # S4 method for class 'SVM,DatasetExperiment' model_predict(M, D)  # S4 method for class 'vec_norm,DatasetExperiment' model_predict(M, D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_predict.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Model prediction — model_predict,DFA,DatasetExperiment-method","text":"M model object D DatasetExperiment object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_predict.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Model prediction — model_predict,DFA,DatasetExperiment-method","text":"Returns modified model object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_predict.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Model prediction — model_predict,DFA,DatasetExperiment-method","text":"","code":"M = example_model() M = model_predict(M,iris_DatasetExperiment())"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_reverse.html","id":null,"dir":"Reference","previous_headings":"","what":"Reverse preprocessing — model_reverse,autoscale,DatasetExperiment-method","title":"Reverse preprocessing — model_reverse,autoscale,DatasetExperiment-method","text":"Reverse effect preprocessing step DatasetExperiment.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_reverse.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reverse preprocessing — model_reverse,autoscale,DatasetExperiment-method","text":"","code":"# S4 method for class 'autoscale,DatasetExperiment' model_reverse(M, D)  # S4 method for class 'mean_centre,DatasetExperiment' model_reverse(M, D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_reverse.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reverse preprocessing — model_reverse,autoscale,DatasetExperiment-method","text":"M model object D DatasetExperiment object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_reverse.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reverse preprocessing — model_reverse,autoscale,DatasetExperiment-method","text":"Returns modified DatasetExperiment object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_reverse.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reverse preprocessing — model_reverse,autoscale,DatasetExperiment-method","text":"","code":"M = example_model() D = model_reverse(M,iris_DatasetExperiment())"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_train.html","id":null,"dir":"Reference","previous_headings":"","what":"Train a model — model_train,DFA,DatasetExperiment-method","title":"Train a model — model_train,DFA,DatasetExperiment-method","text":"Trains model using input DatasetExperiment","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_train.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Train a model — model_train,DFA,DatasetExperiment-method","text":"","code":"# S4 method for class 'DFA,DatasetExperiment' model_train(M, D)  # S4 method for class 'PCA,DatasetExperiment' model_train(M, D)  # S4 method for class 'PLSR,DatasetExperiment' model_train(M, D)  # S4 method for class 'PLSDA,DatasetExperiment' model_train(M, D)  # S4 method for class 'autoscale,DatasetExperiment' model_train(M, D)  # S4 method for class 'blank_filter,DatasetExperiment' model_train(M, D)  # S4 method for class 'constant_sum_norm,DatasetExperiment' model_train(M, D)  # S4 method for class 'dratio_filter,DatasetExperiment' model_train(M, D)  # S4 method for class 'filter_by_name,DatasetExperiment' model_train(M, D)  # S4 method for class 'filter_na_count,DatasetExperiment' model_train(M, D)  # S4 method for class 'filter_smeta,DatasetExperiment' model_train(M, D)  # S4 method for class 'glog_transform,DatasetExperiment' model_train(M, D)  # S4 method for class 'linear_model,DatasetExperiment' model_train(M, D)  # S4 method for class 'mean_centre,DatasetExperiment' model_train(M, D)  # S4 method for class 'mv_feature_filter,DatasetExperiment' model_train(M, D)  # S4 method for class 'mv_sample_filter,DatasetExperiment' model_train(M, D)  # S4 method for class 'OPLSR,DatasetExperiment' model_train(M, D)  # S4 method for class 'OPLSDA,DatasetExperiment' model_train(M, D)  # S4 method for class 'pareto_scale,DatasetExperiment' model_train(M, D)  # S4 method for class 'pqn_norm,DatasetExperiment' model_train(M, D)  # S4 method for class 'SVM,DatasetExperiment' model_train(M, D)  # S4 method for class 'vec_norm,DatasetExperiment' model_train(M, D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_train.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Train a model — model_train,DFA,DatasetExperiment-method","text":"M model object D DatasetExperiment object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_train.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Train a model — model_train,DFA,DatasetExperiment-method","text":"Returns modified model object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/model_train.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Train a model — model_train,DFA,DatasetExperiment-method","text":"","code":"M = example_model() M = model_train(M,iris_DatasetExperiment())"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_boxplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Missing value boxplots — mv_boxplot","title":"Missing value boxplots — mv_boxplot","text":"Boxplots number missing values per sample/feature.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_boxplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Missing value boxplots — mv_boxplot","text":"","code":"mv_boxplot(   label_outliers = TRUE,   by_sample = TRUE,   factor_name,   show_counts = TRUE,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_boxplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Missing value boxplots — mv_boxplot","text":"label_outliers (logical) Label outliers. Allowed values limited following: \"TRUE\": Sample labels potential outliers displayed plot. \"FALSE\": Sample labels included plot. default TRUE. by_sample (logical) Plot sample feature. Allowed values limited following: \"TRUE\": Missing values plotted per sample. \"FALSE\": Missing values plotted per feature. default TRUE. factor_name (character) name sample-meta column use. show_counts (logical) Show counts. Allowed values limited following: \"TRUE\": number samples box displayed. \"FALSE\": number samples box displayed. default TRUE. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_boxplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Missing value boxplots — mv_boxplot","text":"  mv_boxplot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_boxplot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Missing value boxplots — mv_boxplot","text":"mv_boxplot object inherits following struct classes: [mv_boxplot] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_boxplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Missing value boxplots — mv_boxplot","text":"","code":"M = mv_boxplot(       label_outliers = FALSE,       by_sample = FALSE,       factor_name = \"V1\",       show_counts = FALSE)  D = MTBLS79_DatasetExperiment() C = mv_boxplot(factor_name='Class') chart_plot(C,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_feature_filter.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter features by missing values — mv_feature_filter","title":"Filter features by missing values — mv_feature_filter","text":"Removes features percentage non-missing values falls threshold.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_feature_filter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter features by missing values — mv_feature_filter","text":"","code":"mv_feature_filter(   threshold = 20,   qc_label = \"QC\",   method = \"QC\",   factor_name,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_feature_filter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter features by missing values — mv_feature_filter","text":"threshold (numeric) minimum percentage non-missing values. default 20. qc_label (character) label used identify QC/group samples using \"QC\" (within named group) filtering method. default \"QC\". method (character) Filtering method. Allowed values limited following: \"within_all\": Features removed threshold non-missing values met groups. \"within_one\": Features removed threshold non-missing values met group. \"QC\": Features removed threshold non-missing values met named group. \"across\": filter applied ignoring sample group. default \"QC\". factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_feature_filter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter features by missing values — mv_feature_filter","text":" mv_feature_filter object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_feature_filter.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Filter features by missing values — mv_feature_filter","text":"object makes use functionality following packages: pmp","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_feature_filter.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Filter features by missing values — mv_feature_filter","text":"mv_feature_filter object inherits following struct classes: [mv_feature_filter] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_feature_filter.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Filter features by missing values — mv_feature_filter","text":"Jankevics , Lloyd GR, Weber RJM (????). pmp: Peak Matrix Processing signal batch correction metabolomics datasets. R package version 1.15.1.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_feature_filter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Filter features by missing values — mv_feature_filter","text":"","code":"M = mv_feature_filter(       threshold = 20,       qc_label = \"QC\",       method = \"QC\",       factor_name = \"V1\")  D = iris_DatasetExperiment() M = mv_feature_filter(factor_name='Species',qc_label='versicolor') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_feature_filter_hist.html","id":null,"dir":"Reference","previous_headings":"","what":"Histogram of missing values per feature — mv_feature_filter_hist","title":"Histogram of missing values per feature — mv_feature_filter_hist","text":"histogram proportion missing values per feature.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_feature_filter_hist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Histogram of missing values per feature — mv_feature_filter_hist","text":"","code":"mv_feature_filter_hist(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_feature_filter_hist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Histogram of missing values per feature — mv_feature_filter_hist","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_feature_filter_hist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Histogram of missing values per feature — mv_feature_filter_hist","text":"  mv_feature_filter_hist  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_feature_filter_hist.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Histogram of missing values per feature — mv_feature_filter_hist","text":"mv_feature_filter_hist object inherits following struct classes: [mv_feature_filter_hist] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_feature_filter_hist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Histogram of missing values per feature — mv_feature_filter_hist","text":"","code":"M = mv_feature_filter_hist()  C = mv_feature_filter_hist()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_histogram.html","id":null,"dir":"Reference","previous_headings":"","what":"Missing value histogram — mv_histogram","title":"Missing value histogram — mv_histogram","text":"histogram numbers missing values per sample/feature","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_histogram.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Missing value histogram — mv_histogram","text":"","code":"mv_histogram(label_outliers = TRUE, by_sample = TRUE, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_histogram.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Missing value histogram — mv_histogram","text":"label_outliers (logical) Label outliers. Allowed values limited following: \"TRUE\": Sample labels potential outliers displayed plot. \"FALSE\": Sample labels included plot. default TRUE. by_sample (logical) Plot sample feature. Allowed values limited following: \"TRUE\": Missing values plotted per sample. \"FALSE\": Missing values plotted per feature. default TRUE. ... additional slots values passed struct_class","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_histogram.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Missing value histogram — mv_histogram","text":"  mv_histogram  object. object output slots. See chart_plot struct package plot chart object. struct object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_histogram.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Missing value histogram — mv_histogram","text":"mv_histogram object inherits following struct classes: [mv_histogram] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_histogram.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Missing value histogram — mv_histogram","text":"","code":"M = mv_histogram(       label_outliers = FALSE,       by_sample = FALSE)  D = MTBLS79_DatasetExperiment() C = mv_histogram(label_outliers=FALSE,by_sample=FALSE) chart_plot(C,D) #> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`. #> Warning: Removed 2 rows containing missing values or values outside the scale range #> (`geom_bar()`)."},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_sample_filter.html","id":null,"dir":"Reference","previous_headings":"","what":"Missing value sample filter — mv_sample_filter","title":"Missing value sample filter — mv_sample_filter","text":"Removes samples percent number missing values exceeds threshold.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_sample_filter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Missing value sample filter — mv_sample_filter","text":"","code":"mv_sample_filter(mv_threshold = 20, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_sample_filter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Missing value sample filter — mv_sample_filter","text":"mv_threshold (numeric) maximum percentage features missing values sample. default 20. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_sample_filter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Missing value sample filter — mv_sample_filter","text":" mv_sample_filter object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_sample_filter.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Missing value sample filter — mv_sample_filter","text":"object makes use functionality following packages: pmp","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_sample_filter.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Missing value sample filter — mv_sample_filter","text":"mv_sample_filter object inherits following struct classes: [mv_sample_filter] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_sample_filter.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Missing value sample filter — mv_sample_filter","text":"Jankevics , Lloyd GR, Weber RJM (????). pmp: Peak Matrix Processing signal batch correction metabolomics datasets. R package version 1.15.1.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_sample_filter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Missing value sample filter — mv_sample_filter","text":"","code":"M = mv_sample_filter(       mv_threshold = 20)  C = mv_sample_filter()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_sample_filter_hist.html","id":null,"dir":"Reference","previous_headings":"","what":"Histogram of missing values per sample — mv_sample_filter_hist","title":"Histogram of missing values per sample — mv_sample_filter_hist","text":"histogram proportion missing values per sample","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_sample_filter_hist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Histogram of missing values per sample — mv_sample_filter_hist","text":"","code":"mv_sample_filter_hist(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_sample_filter_hist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Histogram of missing values per sample — mv_sample_filter_hist","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_sample_filter_hist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Histogram of missing values per sample — mv_sample_filter_hist","text":"  mv_sample_filter_hist  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_sample_filter_hist.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Histogram of missing values per sample — mv_sample_filter_hist","text":"mv_sample_filter_hist object inherits following struct classes: [mv_sample_filter_hist] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/mv_sample_filter_hist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Histogram of missing values per sample — mv_sample_filter_hist","text":"","code":"M = mv_sample_filter_hist()  C = mv_sample_filter_hist()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/nroot_transform.html","id":null,"dir":"Reference","previous_headings":"","what":"nth root transform — nroot_transform","title":"nth root transform — nroot_transform","text":"values data matrix transformed raising power 1/n.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/nroot_transform.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"nth root transform — nroot_transform","text":"","code":"nroot_transform(root = 2, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/nroot_transform.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"nth root transform — nroot_transform","text":"root (numeric) nth root used transform. default 2. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/nroot_transform.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"nth root transform — nroot_transform","text":" nroot_transform object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/nroot_transform.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"nth root transform — nroot_transform","text":"nroot_transform object inherits following struct classes: [nroot_transform] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/nroot_transform.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"nth root transform — nroot_transform","text":"","code":"M = nroot_transform(       root = 2)  M = nroot_transform()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ontology_cache.html","id":null,"dir":"Reference","previous_headings":"","what":"ontology cache — ontology_cache","title":"ontology cache — ontology_cache","text":"cached list ontology terms obtained ontology lookup service (OLS) ontology terms specified objects structToolbox.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ontology_cache.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ontology cache — ontology_cache","text":"","code":"ontology_cache()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ontology_cache.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"ontology cache — ontology_cache","text":"list cached ontology terms","code":""},{"path":[]},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ontology_cache.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ontology cache — ontology_cache","text":"","code":"cache = ontology_cache()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pairs_filter.html","id":null,"dir":"Reference","previous_headings":"","what":"Pairs filter — pairs_filter","title":"Pairs filter — pairs_filter","text":"filter used study designs paired sampling ensure  measurements source (e.g. patient) represented factor levels interactions.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pairs_filter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pairs filter — pairs_filter","text":"","code":"pairs_filter(factor_name, sample_id, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pairs_filter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pairs filter — pairs_filter","text":"factor_name (character) name sample-meta column use. sample_id (character) Name sample meta column containing sample identifiers. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pairs_filter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pairs filter — pairs_filter","text":" pairs_filter object following output slots: struct object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pairs_filter.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Pairs filter — pairs_filter","text":"pairs_filter object inherits following struct classes: [pairs_filter] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pairs_filter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pairs filter — pairs_filter","text":"","code":"M = pairs_filter(       factor_name = \"V1\",       sample_id = \"V1\")  M=pairs_filter(factor_name='Class',sample_id='ids')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pareto_scale.html","id":null,"dir":"Reference","previous_headings":"","what":"Pareto scaling — pareto_scale","title":"Pareto scaling — pareto_scale","text":"mean sample subtracted samples scaled square root standard deviation. transformed data zero mean.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pareto_scale.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pareto scaling — pareto_scale","text":"","code":"pareto_scale(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pareto_scale.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pareto scaling — pareto_scale","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pareto_scale.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pareto scaling — pareto_scale","text":" pareto_scale object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pareto_scale.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Pareto scaling — pareto_scale","text":"pareto_scale object inherits following struct classes: [pareto_scale] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pareto_scale.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pareto scaling — pareto_scale","text":"","code":"M = pareto_scale()  D = iris_DatasetExperiment() M = pareto_scale() M = model_train(M,D) M = model_predict(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_biplot.html","id":null,"dir":"Reference","previous_headings":"","what":"PCA biplot — pca_biplot","title":"PCA biplot — pca_biplot","text":"scatter plot selected principal component scores overlaid corresponding principal component loadings.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_biplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PCA biplot — pca_biplot","text":"","code":"pca_biplot(   components = c(1, 2),   points_to_label = \"none\",   factor_name,   scale_factor = 0.95,   style = \"points\",   label_features = FALSE,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_biplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PCA biplot — pca_biplot","text":"components (numeric) principal components used generate plot. default c(1, 2). points_to_label (character) points_to_label. Allowed values limited following: \"none\": samples labelled plot. \"\": samples labelled plot. \"outliers\": Potential outliers labelled plot. default \"none\". factor_name (character) name sample-meta column use. scale_factor (numeric) scaling factor applied loadings. default 0.95. style (character) Plot style. Allowed values limited following: \"points\": Loadings scores plotted scatter plot. \"arrows\": loadings plotted arrow vectors. default \"points\". label_features (logical) Add feature labels. Allowed values limited following: \"TRUE\": Features labelled. \"FALSE\": Features labelled. default FALSE. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_biplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PCA biplot — pca_biplot","text":"  pca_biplot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_biplot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"PCA biplot — pca_biplot","text":"pca_biplot object inherits following struct classes: [pca_biplot] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_biplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PCA biplot — pca_biplot","text":"","code":"M = pca_biplot(       components = c(1, 2),       points_to_label = \"none\",       factor_name = \"V1\",       scale_factor = 0.95,       style = \"points\",       label_features = FALSE)  C = pca_biplot(factor_name='Species')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_correlation_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"PCA correlation plot — pca_correlation_plot","title":"PCA correlation plot — pca_correlation_plot","text":"plot correlation variables/features selected principal component scores. Features high correlation well represented selected component(s)","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_correlation_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PCA correlation plot — pca_correlation_plot","text":"","code":"pca_correlation_plot(components = c(1, 2), ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_correlation_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PCA correlation plot — pca_correlation_plot","text":"components (numeric) Principal Components used generate plot. default c(1, 2). ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_correlation_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PCA correlation plot — pca_correlation_plot","text":"  pca_correlation_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_correlation_plot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"PCA correlation plot — pca_correlation_plot","text":"pca_correlation_plot object inherits following struct classes: [pca_correlation_plot] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_correlation_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PCA correlation plot — pca_correlation_plot","text":"","code":"M = pca_correlation_plot(       components = c(1, 2))  C = pca_correlation_plot()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_dstat_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"d-statistic plot — pca_dstat_plot","title":"d-statistic plot — pca_dstat_plot","text":"bar chart d-statistics samples input PCA model. Samples indicated threshold considered outlying.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_dstat_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"d-statistic plot — pca_dstat_plot","text":"","code":"pca_dstat_plot(number_components = 2, alpha = 0.05, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_dstat_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"d-statistic plot — pca_dstat_plot","text":"number_components (numeric) number principal components use. default 2. alpha (numeric) confidence threshold rejecting samples based d-statistic. default 0.05. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_dstat_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"d-statistic plot — pca_dstat_plot","text":"  pca_dstat_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_dstat_plot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"d-statistic plot — pca_dstat_plot","text":"pca_dstat_plot object inherits following struct classes: [pca_dstat_plot] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_dstat_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"d-statistic plot — pca_dstat_plot","text":"","code":"M = pca_dstat_plot(       number_components = 2,       alpha = 0.95)  C = pca_dstat_plot()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_loadings_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"PCA loadings plot — pca_loadings_plot","title":"PCA loadings plot — pca_loadings_plot","text":"barchart (one component) scatter plot (two components) selected principal component loadings.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_loadings_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PCA loadings plot — pca_loadings_plot","text":"","code":"pca_loadings_plot(   components = c(1, 2),   style = \"points\",   label_features = NULL,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_loadings_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PCA loadings plot — pca_loadings_plot","text":"components (numeric) principal components used generate plot. default c(1, 2). style (character) Plot style. Allowed values limited following: \"points\": Loadings scores plotted scatter plot. \"arrows\": loadings plotted arrow vectors. default \"points\". label_features (character, NULL) Feature labels. Allowed values limited following: \"character()\": vector labels features. \"NULL\": labels. \"row.names\": Labels extracted column names data matrix. default NULL. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_loadings_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PCA loadings plot — pca_loadings_plot","text":"  pca_loadings_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_loadings_plot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"PCA loadings plot — pca_loadings_plot","text":"pca_loadings_plot object inherits following struct classes: [pca_loadings_plot] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_loadings_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PCA loadings plot — pca_loadings_plot","text":"","code":"M = pca_loadings_plot(       components = c(1, 2),       style = \"points\",       label_features = NULL)  C = pca_loadings_plot()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_scores_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"PCA scores plot — pca_scores_plot","title":"PCA scores plot — pca_scores_plot","text":"Plots 2d scatter plot selected components","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_scores_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PCA scores plot — pca_scores_plot","text":"","code":"pca_scores_plot(   xcol = \"PC1\",   ycol = \"PC2\",   points_to_label = \"none\",   factor_name,   ellipse = \"all\",   ellipse_type = \"norm\",   ellipse_confidence = 0.95,   label_filter = character(0),   label_factor = \"rownames\",   label_size = 3.88,   components = NULL,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_scores_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PCA scores plot — pca_scores_plot","text":"xcol (numeric, integer, character) column name, index, data plot x-axis. default \"PC1\". ycol (numeric, integer, character) column name, index, data plot y-axis. default \"PC2\". points_to_label (character) Points label. Allowed values limited following: \"none\": samples labels displayed. \"\": labels samples displayed. \"outliers\": Labels potential outlier samples displayed. default \"none\". factor_name (character) name sample-meta column use. ellipse (character) Plot ellipses. Allowed values limited following: \"\": Ellipses plotted groups samples. \"group\": Ellipses plotted groups. \"none\": Ellipses included plot. \"sample\": ellipse plotted samples (ignoring group). default \"\". ellipse_type (character) Type ellipse. Allowed values limited following: \"norm\": Multivariate normal (p = 0.95). \"t\": Multivariate t (p = 0.95). default \"norm\". ellipse_confidence (numeric) confidence level plotting ellipses. default 0.95. label_filter (character) Labels plotted named groups. zero-length groups included. default character(0). label_factor (character) column name sample_meta use labelling samples plot. \"rownames\" use row names sample_meta. default \"rownames\". label_size (numeric) text size labels. Note Font Units. default 3.88. components (numeric, integer, NULL) principal components used generate plot. provided parameter overrides xcol ycol params. default NULL. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_scores_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PCA scores plot — pca_scores_plot","text":"  pca_scores_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_scores_plot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"PCA scores plot — pca_scores_plot","text":"pca_scores_plot object inherits following struct classes: [pca_scores_plot] >> [scatter_chart] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_scores_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PCA scores plot — pca_scores_plot","text":"","code":"M = pca_scores_plot(       components = NULL,       xcol = 1,       ycol = 2,       points_to_label = \"none\",       factor_name = \"V1\",       ellipse = \"all\",       label_filter = character(0),       label_factor = \"rownames\",       label_size = 3.88,       ellipse_type = \"norm\",       ellipse_confidence = 0.95)  D = iris_DatasetExperiment() M = mean_centre() + PCA() M = model_apply(M,D) C = pca_scores_plot(factor_name = 'Species') chart_plot(C,M[2])"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_scree_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Scree plot — pca_scree_plot","title":"Scree plot — pca_scree_plot","text":"plot percent variance cumulative percent variance components PCA model.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_scree_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Scree plot — pca_scree_plot","text":"","code":"pca_scree_plot(max_pc = 15, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_scree_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Scree plot — pca_scree_plot","text":"max_pc (numeric, integer) maximum number components include plot. default 15. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_scree_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Scree plot — pca_scree_plot","text":"  pca_scree_plot  object. object output slots. See chart_plot struct package plot chart object. struct object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_scree_plot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Scree plot — pca_scree_plot","text":"pca_scree_plot object inherits following struct classes: [pca_scree_plot] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pca_scree_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Scree plot — pca_scree_plot","text":"","code":"M = pca_scree_plot(       max_pc = 15)  C = pca_scree_plot()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permutation_test.html","id":null,"dir":"Reference","previous_headings":"","what":"Permutation test — permutation_test","title":"Permutation test — permutation_test","text":"permutation test generates \"null\" model randomising response (regression models) group labels (classification models). repeated many times generate distribution performance metrics null model. distribution can compared performance true model. overlap true null model performances model overfitted.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permutation_test.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Permutation test — permutation_test","text":"","code":"permutation_test(number_of_permutations = 50, factor_name, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permutation_test.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Permutation test — permutation_test","text":"number_of_permutations (numeric, integer) number permutations. default 50. factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permutation_test.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Permutation test — permutation_test","text":" permutation_test object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permutation_test.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Permutation test — permutation_test","text":"permutation_test object inherits following struct classes: [permutation_test] >> [resampler] >> [iterator] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permutation_test.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Permutation test — permutation_test","text":"","code":"M = permutation_test(       number_of_permutations = 100,       factor_name = \"V1\")  I=permutation_test(factor_name='Species')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permutation_test_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"permutation_test_plot class — permutation_test_plot","title":"permutation_test_plot class — permutation_test_plot","text":"Plots results permutation test.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permutation_test_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"permutation_test_plot class — permutation_test_plot","text":"","code":"permutation_test_plot(style = \"boxplot\", binwidth = 0.05, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permutation_test_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"permutation_test_plot class — permutation_test_plot","text":"style plot style. One 'boxplot', 'violin', 'histogram', 'density' 'scatter'. binwidth Binwidth \"histogram\" style. Ignored styles. ... additional slots values passed struct_class","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permutation_test_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"permutation_test_plot class — permutation_test_plot","text":"struct object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permutation_test_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"permutation_test_plot class — permutation_test_plot","text":"","code":"C = permutation_test_plot(style='boxplot')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permute_sample_order.html","id":null,"dir":"Reference","previous_headings":"","what":"Permute Sample Order — permute_sample_order","title":"Permute Sample Order — permute_sample_order","text":"order samples data matrix randomly permuted. relationship samples sample meta data maintained.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permute_sample_order.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Permute Sample Order — permute_sample_order","text":"","code":"permute_sample_order(number_of_permutations = 10, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permute_sample_order.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Permute Sample Order — permute_sample_order","text":"number_of_permutations (numeric, integer) number times sample order permuted. default 10. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permute_sample_order.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Permute Sample Order — permute_sample_order","text":" permute_sample_order object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permute_sample_order.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Permute Sample Order — permute_sample_order","text":"permute_sample_order object inherits following struct classes: [permute_sample_order] >> [resampler] >> [iterator] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/permute_sample_order.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Permute Sample Order — permute_sample_order","text":"","code":"M = permute_sample_order(       number_of_permutations = 100)  C = permute_sample_order()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_regcoeff_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"pls_regcoeff_plot class — pls_regcoeff_plot","title":"pls_regcoeff_plot class — pls_regcoeff_plot","text":"Plots regression coefficients PLSDA model. Plots regression coefficient scores PLSDA model","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_regcoeff_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"pls_regcoeff_plot class — pls_regcoeff_plot","text":"","code":"pls_regcoeff_plot(ycol = 1, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_regcoeff_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"pls_regcoeff_plot class — pls_regcoeff_plot","text":"ycol (character, numeric, integer) Y column plot. default 1. ... additional slots values passed struct_class","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_regcoeff_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"pls_regcoeff_plot class — pls_regcoeff_plot","text":"  pls_regcoeff_plot  object. object output slots. See chart_plot struct package plot chart object. struct object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_regcoeff_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"pls_regcoeff_plot class — pls_regcoeff_plot","text":"object makes use functionality following packages: pls ggplot2","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_regcoeff_plot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"pls_regcoeff_plot class — pls_regcoeff_plot","text":"pls_regcoeff_plot object inherits following struct classes: [pls_regcoeff_plot] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_regcoeff_plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"pls_regcoeff_plot class — pls_regcoeff_plot","text":"Liland K, Mevik B, Wehrens R (2023). pls: Partial Least Squares Principal Component Regression. R package version 2.8-3, https://CRAN.R-project.org/package=pls. Wickham H (2016). ggplot2: Elegant Graphics Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_regcoeff_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"pls_regcoeff_plot class — pls_regcoeff_plot","text":"","code":"M = pls_regcoeff_plot(       ycol = 1)  D = iris_DatasetExperiment() M = mean_centre()+PLSDA(factor_name='Species') M = model_apply(M,D)  C = pls_regcoeff_plot(ycol='setosa') chart_plot(C,M[2])"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_scores_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"PLSDA scores plot — pls_scores_plot","title":"PLSDA scores plot — pls_scores_plot","text":"scatter plot selected PLSDA scores.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_scores_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PLSDA scores plot — pls_scores_plot","text":"","code":"pls_scores_plot(   xcol = \"LV1\",   ycol = \"LV2\",   points_to_label = \"none\",   factor_name,   ellipse = \"all\",   ellipse_type = \"norm\",   ellipse_confidence = 0.95,   label_filter = character(0),   label_factor = \"rownames\",   label_size = 3.88,   components = NULL,   ... )  plsda_scores_plot(   xcol = \"LV1\",   ycol = \"LV2\",   points_to_label = \"none\",   factor_name,   ellipse = \"all\",   ellipse_type = \"norm\",   ellipse_confidence = 0.95,   label_filter = character(0),   label_factor = \"rownames\",   label_size = 3.88,   components = NULL,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_scores_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PLSDA scores plot — pls_scores_plot","text":"xcol (numeric, integer, character) column name, index, data plot x-axis. default \"LV1\". ycol (numeric, integer, character) column name, index, data plot y-axis. default \"LV2\". points_to_label (character) Points label. Allowed values limited following: \"none\": samples labels displayed. \"\": labels samples displayed. \"outliers\": Labels potential outlier samples displayed. default \"none\". factor_name (character) name sample-meta column use. ellipse (character) Plot ellipses. Allowed values limited following: \"\": Ellipses plotted groups samples. \"group\": Ellipses plotted groups. \"none\": Ellipses included plot. \"sample\": ellipse plotted samples (ignoring group). default \"\". ellipse_type (character) Type ellipse. Allowed values limited following: \"norm\": Multivariate normal (p = 0.95). \"t\": Multivariate t (p = 0.95). default \"norm\". ellipse_confidence (numeric) confidence level plotting ellipses. default 0.95. label_filter (character) Labels plotted named groups. zero-length groups included. default character(0). label_factor (character) column name sample_meta use labelling samples plot. \"rownames\" use row names sample_meta. default \"rownames\". label_size (numeric) text size labels. Note Font Units. default 3.88. components (numeric, integer, NULL) principal components used generate plot. provided parameter overrides xcol ycol params. default NULL. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_scores_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PLSDA scores plot — pls_scores_plot","text":"  pls_scores_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_scores_plot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"PLSDA scores plot — pls_scores_plot","text":"pls_scores_plot object inherits following struct classes: [pls_scores_plot] >> [scatter_chart] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_scores_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PLSDA scores plot — pls_scores_plot","text":"","code":"M = pls_scores_plot(       components = NULL,       xcol = 1,       ycol = 2,       points_to_label = \"none\",       factor_name = \"V1\",       ellipse = \"all\",       label_filter = character(0),       label_factor = \"rownames\",       label_size = 3.88,       ellipse_type = \"norm\",       ellipse_confidence = 0.95)  D = iris_DatasetExperiment() M = mean_centre()+PLSDA(factor_name='Species') M = model_apply(M,D)  C = pls_scores_plot(factor_name='Species') chart_plot(C,M[2])"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_vip_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"PLSDA VIP plot — pls_vip_plot","title":"PLSDA VIP plot — pls_vip_plot","text":"plot Variable Importance Projection (VIP) scores PLSDA model.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_vip_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PLSDA VIP plot — pls_vip_plot","text":"","code":"pls_vip_plot(threshold = 1, ycol = 1, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_vip_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PLSDA VIP plot — pls_vip_plot","text":"threshold (numeric, integer) threshold indicating significant features. default 1. ycol (character, numeric, integer) column Y block plotted. default 1. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_vip_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PLSDA VIP plot — pls_vip_plot","text":"  pls_vip_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_vip_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PLSDA VIP plot — pls_vip_plot","text":"object makes use functionality following packages: pls ggplot2","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_vip_plot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"PLSDA VIP plot — pls_vip_plot","text":"pls_vip_plot object inherits following struct classes: [pls_vip_plot] >> [chart] >> [stato] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_vip_plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"PLSDA VIP plot — pls_vip_plot","text":"Liland K, Mevik B, Wehrens R (2023). pls: Partial Least Squares Principal Component Regression. R package version 2.8-3, https://CRAN.R-project.org/package=pls. Wickham H (2016). ggplot2: Elegant Graphics Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pls_vip_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PLSDA VIP plot — pls_vip_plot","text":"","code":"M = pls_vip_plot(       threshold = 1,       ycol = 1)  D = iris_DatasetExperiment() M = mean_centre()+PLSDA(factor_name='Species') M = model_apply(M,D)  C = pls_vip_plot(ycol='setosa') chart_plot(C,M[2])"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_feature_importance_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"PLSDA feature importance summary plot — plsda_feature_importance_plot","title":"PLSDA feature importance summary plot — plsda_feature_importance_plot","text":"plot selected feature significance metric PLSDA model top selected features.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_feature_importance_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PLSDA feature importance summary plot — plsda_feature_importance_plot","text":"","code":"plsda_feature_importance_plot(n_features = 30, metric = \"vip\", ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_feature_importance_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PLSDA feature importance summary plot — plsda_feature_importance_plot","text":"n_features (numeric, integer) number features include summary. default 30. metric (character) Metric plot. Allowed values limited following: \"sr\": Plot Selectivity Ratio. \"sr_pvalue\": Plot SR p-values. \"vip\": Plot Variable Importance Projection scores. default \"vip\". ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_feature_importance_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PLSDA feature importance summary plot — plsda_feature_importance_plot","text":"  plsda_feature_importance_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_feature_importance_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PLSDA feature importance summary plot — plsda_feature_importance_plot","text":"object makes use functionality following packages: pls ggplot2 reshape2 cowplot","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_feature_importance_plot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"PLSDA feature importance summary plot — plsda_feature_importance_plot","text":"plsda_feature_importance_plot object inherits following struct classes: [plsda_feature_importance_plot] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_feature_importance_plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"PLSDA feature importance summary plot — plsda_feature_importance_plot","text":"Liland K, Mevik B, Wehrens R (2023). pls: Partial Least Squares Principal Component Regression. R package version 2.8-3, https://CRAN.R-project.org/package=pls. Wickham H (2016). ggplot2: Elegant Graphics Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org. Wickham H (2007). \"Reshaping Data reshape Package.\" Journal Statistical Software, 21(12), 1-20. http://www.jstatsoft.org/v21/i12/. Wilke C (2024). cowplot: Streamlined Plot Theme Plot Annotations 'ggplot2'. R package version 1.1.3, https://CRAN.R-project.org/package=cowplot.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_feature_importance_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PLSDA feature importance summary plot — plsda_feature_importance_plot","text":"","code":"M = plsda_feature_importance_plot(       n_features = 50,       metric = \"vip\")  D = iris_DatasetExperiment() M = mean_centre()+PLSDA(factor_name='Species') M = model_apply(M,D)  C = plsda_feature_importance_plot(n_features=30,metric='vip') chart_plot(C,M[2])"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_predicted_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"PLSDA predicted plot — plsda_predicted_plot","title":"PLSDA predicted plot — plsda_predicted_plot","text":"plot regression coefficients PLSDA model.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_predicted_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PLSDA predicted plot — plsda_predicted_plot","text":"","code":"plsda_predicted_plot(factor_name, style = \"boxplot\", ycol = 1, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_predicted_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PLSDA predicted plot — plsda_predicted_plot","text":"factor_name (character) name sample-meta column use. style (character) Plot style. Allowed values limited following: \"boxplot\": boxplot. \"violin\": violin plot. \"density\": density plot. default \"boxplot\". ycol (character, numeric, integer) column Y block plotted. default 1. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_predicted_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PLSDA predicted plot — plsda_predicted_plot","text":"  plsda_predicted_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_predicted_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PLSDA predicted plot — plsda_predicted_plot","text":"object makes use functionality following packages: pls ggplot2","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_predicted_plot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"PLSDA predicted plot — plsda_predicted_plot","text":"plsda_predicted_plot object inherits following struct classes: [plsda_predicted_plot] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_predicted_plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"PLSDA predicted plot — plsda_predicted_plot","text":"Liland K, Mevik B, Wehrens R (2023). pls: Partial Least Squares Principal Component Regression. R package version 2.8-3, https://CRAN.R-project.org/package=pls. Wickham H (2016). ggplot2: Elegant Graphics Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_predicted_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PLSDA predicted plot — plsda_predicted_plot","text":"","code":"M = plsda_predicted_plot(       factor_name = \"V1\",       style = \"boxplot\",       ycol = 1)  D = iris_DatasetExperiment() M = mean_centre()+PLSDA(factor_name='Species') M = model_apply(M,D)  C = plsda_predicted_plot(factor_name='Species') chart_plot(C,M[2])"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_roc_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"PLSDA ROC plot — plsda_roc_plot","title":"PLSDA ROC plot — plsda_roc_plot","text":"Receiver Operator Characteristic (ROC) plot PLSDA models computed adjusting threshold assigning group labels PLS predictions.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_roc_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PLSDA ROC plot — plsda_roc_plot","text":"","code":"plsda_roc_plot(factor_name, ycol = 1, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_roc_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PLSDA ROC plot — plsda_roc_plot","text":"factor_name (character) name sample-meta column use. ycol (character, numeric, integer) column Y block plotted. default 1. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_roc_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PLSDA ROC plot — plsda_roc_plot","text":"  plsda_roc_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_roc_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PLSDA ROC plot — plsda_roc_plot","text":"object makes use functionality following packages: pls ggplot2","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_roc_plot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"PLSDA ROC plot — plsda_roc_plot","text":"plsda_roc_plot object inherits following struct classes: [plsda_roc_plot] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_roc_plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"PLSDA ROC plot — plsda_roc_plot","text":"Liland K, Mevik B, Wehrens R (2023). pls: Partial Least Squares Principal Component Regression. R package version 2.8-3, https://CRAN.R-project.org/package=pls. Wickham H (2016). ggplot2: Elegant Graphics Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsda_roc_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PLSDA ROC plot — plsda_roc_plot","text":"","code":"M = plsda_roc_plot(       factor_name = \"V1\",       ycol = 1)  D = iris_DatasetExperiment() M = mean_centre()+PLSDA(factor_name='Species') M = model_apply(M,D)  C = plsda_roc_plot(factor_name='Species') chart_plot(C,M[2])"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_cook_dist.html","id":null,"dir":"Reference","previous_headings":"","what":"Cook's distance barchart — plsr_cook_dist","title":"Cook's distance barchart — plsr_cook_dist","text":"barchart Cook's distance sample used train PLSR model. Cook's distance used estimate influence sample model can used identify potential outliers.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_cook_dist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cook's distance barchart — plsr_cook_dist","text":"","code":"plsr_cook_dist(ycol = 1, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_cook_dist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cook's distance barchart — plsr_cook_dist","text":"ycol (numeric, integer, character) y-block column plot. default 1. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_cook_dist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cook's distance barchart — plsr_cook_dist","text":"  plsr_cook_dist  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_cook_dist.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Cook's distance barchart — plsr_cook_dist","text":"plsr_cook_dist object inherits following struct classes: [plsr_cook_dist] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_cook_dist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cook's distance barchart — plsr_cook_dist","text":"","code":"M = plsr_cook_dist(       ycol = 1)  C = plsr_cook_dist()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_prediction_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"PLSR prediction plot — plsr_prediction_plot","title":"PLSR prediction plot — plsr_prediction_plot","text":"scatter plot true response values predicted values PLSR model.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_prediction_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PLSR prediction plot — plsr_prediction_plot","text":"","code":"plsr_prediction_plot(ycol = 1, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_prediction_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PLSR prediction plot — plsr_prediction_plot","text":"ycol (numeric, integer, character) y-block column plot. default 1. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_prediction_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PLSR prediction plot — plsr_prediction_plot","text":"  plsr_prediction_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_prediction_plot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"PLSR prediction plot — plsr_prediction_plot","text":"plsr_prediction_plot object inherits following struct classes: [plsr_prediction_plot] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_prediction_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PLSR prediction plot — plsr_prediction_plot","text":"","code":"M = plsr_prediction_plot(       ycol = 1)  C = plsr_prediction_plot()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_qq_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"PLSR QQ plot — plsr_qq_plot","title":"PLSR QQ plot — plsr_qq_plot","text":"plot quantiles residuals PLSR model quantiles normal distribution.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_qq_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PLSR QQ plot — plsr_qq_plot","text":"","code":"plsr_qq_plot(ycol = 1, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_qq_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PLSR QQ plot — plsr_qq_plot","text":"ycol (numeric, integer, character) y-block column plot. default 1. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_qq_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PLSR QQ plot — plsr_qq_plot","text":"  plsr_qq_plot  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_qq_plot.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"PLSR QQ plot — plsr_qq_plot","text":"plsr_qq_plot object inherits following struct classes: [plsr_qq_plot] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_qq_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PLSR QQ plot — plsr_qq_plot","text":"","code":"M = plsr_qq_plot(       ycol = 1)  C = plsr_qq_plot()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_residual_hist.html","id":null,"dir":"Reference","previous_headings":"","what":"PLSR residuals histogram — plsr_residual_hist","title":"PLSR residuals histogram — plsr_residual_hist","text":"histogram residuals PLSR model.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_residual_hist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PLSR residuals histogram — plsr_residual_hist","text":"","code":"plsr_residual_hist(ycol = 1, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_residual_hist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PLSR residuals histogram — plsr_residual_hist","text":"ycol (numeric, integer, character) y-block column plot. default 1. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_residual_hist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PLSR residuals histogram — plsr_residual_hist","text":"  plsr_residual_hist  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_residual_hist.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"PLSR residuals histogram — plsr_residual_hist","text":"plsr_residual_hist object inherits following struct classes: [plsr_residual_hist] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/plsr_residual_hist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PLSR residuals histogram — plsr_residual_hist","text":"","code":"M = plsr_residual_hist(       ycol = 1)  C = plsr_residual_hist()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pqn_norm.html","id":null,"dir":"Reference","previous_headings":"","what":"Probabilistic Quotient Normalisation (PQN) — pqn_norm","title":"Probabilistic Quotient Normalisation (PQN) — pqn_norm","text":"PQN used normalise differences concentration samples. makes use Quality Control (QC) samples reference. PQN scales median change relative reference order robust changes caused response perturbation.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pqn_norm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Probabilistic Quotient Normalisation (PQN) — pqn_norm","text":"","code":"pqn_norm(   qc_label = \"QC\",   factor_name,   qc_frac = 0,   sample_frac = 0,   ref_method = \"mean\",   ref_mean = NULL,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pqn_norm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Probabilistic Quotient Normalisation (PQN) — pqn_norm","text":"qc_label (character) label used identify QC samples. default \"QC\". factor_name (character) name sample-meta column use. qc_frac (numeric) value 0 1 indicate minimum proportion QC samples feature must present included computing reference. Default qc_frac = 0. . default 0. sample_frac (numeric) value 0 1 indicate minimum proportion samples feature must present considered computing normalisation coefficients. . default 0. ref_method (character) Reference computation method. Allowed values limited following: \"mean\": reference computed mean samples matching qc_label input. \"median\": reference computed median samples matching qc_label_input. default \"mean\". ref_mean (numeric, NULL) single sample use reference normalisation. set NULL reference computed based input parameters (ref_mean, qc_label etc). . default NULL. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pqn_norm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Probabilistic Quotient Normalisation (PQN) — pqn_norm","text":" pqn_norm object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pqn_norm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Probabilistic Quotient Normalisation (PQN) — pqn_norm","text":"object makes use functionality following packages: pmp","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pqn_norm.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Probabilistic Quotient Normalisation (PQN) — pqn_norm","text":"pqn_norm object inherits following struct classes: [pqn_norm] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pqn_norm.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Probabilistic Quotient Normalisation (PQN) — pqn_norm","text":"Jankevics , Lloyd GR, Weber RJM (????). pmp: Peak Matrix Processing signal batch correction metabolomics datasets. R package version 1.15.1.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pqn_norm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Probabilistic Quotient Normalisation (PQN) — pqn_norm","text":"","code":"M = pqn_norm(       qc_label = \"QC\",       factor_name = \"V1\",       qc_frac = 0,       sample_frac = 0,       ref_mean = NULL,       ref_method = \"mean\")  D = iris_DatasetExperiment() M = pqn_norm(factor_name='Species',qc_label='all') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pqn_norm_hist.html","id":null,"dir":"Reference","previous_headings":"","what":"PQN coefficient histogram — pqn_norm_hist","title":"PQN coefficient histogram — pqn_norm_hist","text":"histogram PQN coefficients features","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pqn_norm_hist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PQN coefficient histogram — pqn_norm_hist","text":"","code":"pqn_norm_hist(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pqn_norm_hist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PQN coefficient histogram — pqn_norm_hist","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pqn_norm_hist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PQN coefficient histogram — pqn_norm_hist","text":"  pqn_norm_hist  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pqn_norm_hist.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"PQN coefficient histogram — pqn_norm_hist","text":"pqn_norm_hist object inherits following struct classes: [pqn_norm_hist] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/pqn_norm_hist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PQN coefficient histogram — pqn_norm_hist","text":"","code":"M = pqn_norm_hist()  C = pqn_norm_hist()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/prop_na.html","id":null,"dir":"Reference","previous_headings":"","what":"Fisher's exact test for missing values — prop_na","title":"Fisher's exact test for missing values — prop_na","text":"Fisher's exact test used compare number missing values group. Multiple test corrected p-values computed indicate whether significant difference number missing values across groups feature.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/prop_na.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fisher's exact test for missing values — prop_na","text":"","code":"prop_na(alpha = 0.05, mtc = \"fdr\", factor_name, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/prop_na.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fisher's exact test for missing values — prop_na","text":"alpha (numeric) p-value cutoff determining significance. default 0.05. mtc (character) Multiple test correction method. Allowed values limited following: \"bonferroni\": Bonferroni correction p-values multiplied number comparisons. \"fdr\": Benjamini Hochberg False Discovery Rate correction. \"none\": correction. default \"fdr\". factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/prop_na.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fisher's exact test for missing values — prop_na","text":" prop_na object following output slots: struct object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/prop_na.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Fisher's exact test for missing values — prop_na","text":"prop_na object inherits following struct classes: [prop_na] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/prop_na.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fisher's exact test for missing values — prop_na","text":"","code":"M = prop_na(       alpha = 0.05,       mtc = \"fdr\",       factor_name = \"V1\")  M = prop_na(factor_name='Species')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/r_squared.html","id":null,"dir":"Reference","previous_headings":"","what":"Coefficient of determination (R-squared) — r_squared","title":"Coefficient of determination (R-squared) — r_squared","text":"R-squared metric used assess goodness fit regression models. measures much variance one variable can explained another variable.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/r_squared.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Coefficient of determination (R-squared) — r_squared","text":"","code":"r_squared(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/r_squared.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Coefficient of determination (R-squared) — r_squared","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/r_squared.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Coefficient of determination (R-squared) — r_squared","text":"  r_squared  object. object output slots.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/r_squared.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Coefficient of determination (R-squared) — r_squared","text":"r_squared object inherits following struct classes: [r_squared] >> [metric] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/r_squared.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Coefficient of determination (R-squared) — r_squared","text":"","code":"M = r_squared()  MET = r_squared()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/resample.html","id":null,"dir":"Reference","previous_headings":"","what":"Data resampling — resample","title":"Data resampling — resample","text":"New training sets generated original data selecting samples random. can based levels factor whole dataset.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/resample.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data resampling — resample","text":"","code":"resample(   number_of_iterations = 10,   method = \"split_data\",   factor_name,   p_train = 0.8,   collect = NULL,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/resample.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Data resampling — resample","text":"number_of_iterations (numeric, integer) number training sets generate. default 10. method (character) Resampling method. Allowed values limited following: \"split_data\": Samples training set selected random full dataset. \"stratified_split\": Samples training set randomly selected level chosen factor. \"equal_split\": Samples training set selected random level main factor group sizes equal. default \"split_data\". factor_name (character) name sample-meta column use. p_train (numeric) proportion samples selected training set. default 0.8. collect (NULL, character) name model output collect bootstrap repetitions, addition input metric. default NULL. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/resample.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Data resampling — resample","text":" resample object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/resample.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Data resampling — resample","text":"resample object inherits following struct classes: [resample] >> [resampler] >> [iterator] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/resample.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data resampling — resample","text":"","code":"M = resample(       number_of_iterations = 100,       method = \"split_data\",       factor_name = \"V1\",       p_train = 0.75,       collect = NULL)  I = resample(     number_of_iterations = 10,      factor_name = 'Species',      method = 'split_data',     p_train = 0.8)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/resample_chart.html","id":null,"dir":"Reference","previous_headings":"","what":"resample_chart class — resample_chart","title":"resample_chart class — resample_chart","text":"Plots results resampling.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/resample_chart.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"resample_chart class — resample_chart","text":"","code":"resample_chart(style = \"boxplot\", binwidth = 0.05, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/resample_chart.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"resample_chart class — resample_chart","text":"style plot style. One 'boxplot', 'violin', 'histogram', 'density' 'scatter'. binwidth Binwidth \"histogram\" style. Ignored styles. ... additional slots values passed struct_class","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/resample_chart.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"resample_chart class — resample_chart","text":"struct object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/resample_chart.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"resample_chart class — resample_chart","text":"","code":"C = resample_chart(style='boxplot')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/rsd_filter.html","id":null,"dir":"Reference","previous_headings":"","what":"RSD filter — rsd_filter","title":"RSD filter — rsd_filter","text":"RSD filter calculates relative standard deviation (ratio standard deviation mean) features. feature RSD greater predefined threshold excluded.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/rsd_filter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"RSD filter — rsd_filter","text":"","code":"rsd_filter(rsd_threshold = 20, qc_label = \"QC\", factor_name, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/rsd_filter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"RSD filter — rsd_filter","text":"rsd_threshold (numeric) RSD threshold features removed. default 20. qc_label (character) label used identify QC samples. default \"QC\". factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/rsd_filter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"RSD filter — rsd_filter","text":" rsd_filter object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/rsd_filter.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"RSD filter — rsd_filter","text":"object makes use functionality following packages: pmp","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/rsd_filter.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"RSD filter — rsd_filter","text":"rsd_filter object inherits following struct classes: [rsd_filter] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/rsd_filter.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"RSD filter — rsd_filter","text":"Jankevics , Lloyd GR, Weber RJM (????). pmp: Peak Matrix Processing signal batch correction metabolomics datasets. R package version 1.15.1.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/rsd_filter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"RSD filter — rsd_filter","text":"","code":"M = rsd_filter(       rsd_threshold = 20,       qc_label = \"QC\",       factor_name = \"V1\")  M = rsd_filter(factor_name='Class')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/rsd_filter_hist.html","id":null,"dir":"Reference","previous_headings":"","what":"RSD histogram — rsd_filter_hist","title":"RSD histogram — rsd_filter_hist","text":"histogram calculated RSD values.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/rsd_filter_hist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"RSD histogram — rsd_filter_hist","text":"","code":"rsd_filter_hist(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/rsd_filter_hist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"RSD histogram — rsd_filter_hist","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/rsd_filter_hist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"RSD histogram — rsd_filter_hist","text":"  rsd_filter_hist  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/rsd_filter_hist.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"RSD histogram — rsd_filter_hist","text":"rsd_filter_hist object inherits following struct classes: [rsd_filter_hist] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/rsd_filter_hist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"RSD histogram — rsd_filter_hist","text":"","code":"M = rsd_filter_hist()  C = rsd_filter_hist()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/run.html","id":null,"dir":"Reference","previous_headings":"","what":"Runs an iterator, applying the chosen model multiple times. — run,bootstrap,DatasetExperiment,metric-method","title":"Runs an iterator, applying the chosen model multiple times. — run,bootstrap,DatasetExperiment,metric-method","text":"Running iterator apply iterator number times DatasetExperiment. example, cross-validation model applied multiple times data, splitting training test sets. input metric object can calculated collected iteration output.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/run.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Runs an iterator, applying the chosen model multiple times. — run,bootstrap,DatasetExperiment,metric-method","text":"","code":"# S4 method for class 'bootstrap,DatasetExperiment,metric' run(I, D, MET = NULL)  # S4 method for class 'forward_selection_by_rank,DatasetExperiment,metric' run(I, D, MET)  # S4 method for class 'grid_search_1d,DatasetExperiment,metric' run(I, D, MET)  # S4 method for class 'kfold_xval,DatasetExperiment,metric' run(I, D, MET = NULL)  # S4 method for class 'permutation_test,DatasetExperiment,metric' run(I, D, MET = NULL)  # S4 method for class 'permute_sample_order,DatasetExperiment,metric' run(I, D, MET)  # S4 method for class 'resample,DatasetExperiment,metric' run(I, D, MET)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/run.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Runs an iterator, applying the chosen model multiple times. — run,bootstrap,DatasetExperiment,metric-method","text":"iterator object D DatasetExperiment object MET metric object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/run.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Runs an iterator, applying the chosen model multiple times. — run,bootstrap,DatasetExperiment,metric-method","text":"Modified iterator object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/run.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Runs an iterator, applying the chosen model multiple times. — run,bootstrap,DatasetExperiment,metric-method","text":"","code":"D = iris_DatasetExperiment() # get some data MET = metric()  # use a metric I = example_iterator() # initialise iterator models(I) = example_model() # set the model I = run(I,D,MET) # run #> Warning: no calculation provided for this metric"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/sb_corr.html","id":null,"dir":"Reference","previous_headings":"","what":"Signal/batch correction for mass spectrometry data — sb_corr","title":"Signal/batch correction for mass spectrometry data — sb_corr","text":"Applies Quality Control Robust Spline (QC-RSC) method correct signal drift batch differences mass spectrometry data.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/sb_corr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Signal/batch correction for mass spectrometry data — sb_corr","text":"","code":"sb_corr(   order_col,   batch_col,   qc_col,   smooth = 0,   use_log = TRUE,   min_qc = 4,   qc_label = \"QC\",   spar_lim = c(-1.5, 1.5),   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/sb_corr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Signal/batch correction for mass spectrometry data — sb_corr","text":"order_col (character) column name sample_meta indicating run order samples. batch_col (character) column name sample_meta indicating batch sample measured . qc_col (character) column name sample_meta indicating group sample member . smooth (numeric) amount smoothing applied (0 1). set 0 smoothing parameter estimated using leave-one-cross-validation. default 0. use_log (logical) Log tranformation. Allowed values limited following: \"TRUE\": data log transformed prior performing signal correction. \"FALSE\": Signal correction applied input data. default TRUE. min_qc (numeric) minimum number QC samples required signal correction. default 4. qc_label (character) label used identify QC samples. default \"QC\". spar_lim (numeric) two element vector specifying upper lower limits spar = 0. Allows value spar constrained within limits prevent overfitting. default c(-1.5, 1.5). ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/sb_corr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Signal/batch correction for mass spectrometry data — sb_corr","text":" sb_corr object following output slots: struct object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/sb_corr.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Signal/batch correction for mass spectrometry data — sb_corr","text":"object makes use functionality following packages: pmp","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/sb_corr.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Signal/batch correction for mass spectrometry data — sb_corr","text":"sb_corr object inherits following struct classes: [sb_corr] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/sb_corr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Signal/batch correction for mass spectrometry data — sb_corr","text":"Jankevics , Lloyd GR, Weber RJM (????). pmp: Peak Matrix Processing signal batch correction metabolomics datasets. R package version 1.15.1. Kirwan JA, Broadhurst DI, Davidson RL, Viant MR (2013). \"Characterising correcting batch variation automated direct infusion mass spectrometry (DIMS) metabolomics workflow.\" Analytical Bioanalytical Chemistry, 405(15), 5147-5157.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/sb_corr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Signal/batch correction for mass spectrometry data — sb_corr","text":"","code":"M = sb_corr(       order_col = character(0),       batch_col = character(0),       qc_col = character(0),       smooth = 0,       use_log = FALSE,       min_qc = 4,       qc_label = \"QC\",       spar_lim = c(-1.5, 1.5))  M = sb_corr(order_col='run_order',batch_col='batch_no',qc_col='class')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/scatter_chart.html","id":null,"dir":"Reference","previous_headings":"","what":"Group scatter chart — scatter_chart","title":"Group scatter chart — scatter_chart","text":"Plots 2d scatter plot input data.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/scatter_chart.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Group scatter chart — scatter_chart","text":"","code":"scatter_chart(   xcol = 1,   ycol = 2,   points_to_label = \"none\",   factor_name = \"none\",   ellipse = \"all\",   ellipse_type = \"norm\",   ellipse_confidence = 0.95,   label_filter = character(0),   label_factor = \"rownames\",   label_size = 3.88,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/scatter_chart.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Group scatter chart — scatter_chart","text":"xcol (numeric, integer, character) column name, index, data plot x-axis. default 1. ycol (numeric, integer, character) column name, index, data plot y-axis. default 2. points_to_label (character) Points label. Allowed values limited following: \"none\": samples labels displayed. \"\": labels samples displayed. \"outliers\": Labels potential outlier samples displayed. default \"none\". factor_name (character) name sample-meta column use. default \"none\". ellipse (character) Plot ellipses. Allowed values limited following: \"\": Ellipses plotted groups samples. \"group\": Ellipses plotted groups. \"none\": Ellipses included plot. \"sample\": ellipse plotted samples (ignoring group). default \"\". ellipse_type (character) Type ellipse. Allowed values limited following: \"norm\": Multivariate normal (p = 0.95). \"t\": Multivariate t (p = 0.95). default \"norm\". ellipse_confidence (numeric) confidence level plotting ellipses. default 0.95. label_filter (character) Labels plotted named groups. zero-length groups included. default character(0). label_factor (character) column name sample_meta use labelling samples plot. \"rownames\" use row names sample_meta. default \"rownames\". label_size (numeric) text size labels. Note Font Units. default 3.88. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/scatter_chart.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Group scatter chart — scatter_chart","text":"  scatter_chart  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/scatter_chart.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Group scatter chart — scatter_chart","text":"scatter_chart object inherits following struct classes: [scatter_chart] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/scatter_chart.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Group scatter chart — scatter_chart","text":"","code":"M = scatter_chart(       xcol = 1,       ycol = 2,       points_to_label = \"none\",       factor_name = \"V1\",       ellipse = \"all\",       label_filter = character(0),       label_factor = \"rownames\",       label_size = 3.88,       ellipse_type = \"norm\",       ellipse_confidence = 0.95)  D = iris_DatasetExperiment() C = scatter_chart(         xcol = 'Petal.Width',         ycol = 'Sepal.Width',         factor_name = 'Species'     ) chart_plot(C,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/split_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Split data — split_data","title":"Split data — split_data","text":"data matrix divided two subsets.predefined proportion samples randomly selected training set, remaining samples used test set.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/split_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split data — split_data","text":"","code":"split_data(p_train, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/split_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split data — split_data","text":"p_train (numeric) proportion samples selected training set. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/split_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split data — split_data","text":" split_data object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/split_data.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Split data — split_data","text":"split_data object inherits following struct classes: [split_data] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/split_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split data — split_data","text":"","code":"M = split_data(       p_train = 0.75)  M = split_data(p_train=0.75)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/stratified_split.html","id":null,"dir":"Reference","previous_headings":"","what":"Stratified sampling — stratified_split","title":"Stratified sampling — stratified_split","text":"dataset divided two subsets. predefined proportion samples level factor selected training set, remaining samples used test set. stratification factor level means relative number samples per level approximately equal original dataset.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/stratified_split.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Stratified sampling — stratified_split","text":"","code":"stratified_split(p_train, factor_name, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/stratified_split.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Stratified sampling — stratified_split","text":"p_train (numeric) proportion samples selected training set. factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/stratified_split.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Stratified sampling — stratified_split","text":" stratified_split object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/stratified_split.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Stratified sampling — stratified_split","text":"stratified_split object inherits following struct classes: [stratified_split] >> [split_data] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/stratified_split.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Stratified sampling — stratified_split","text":"","code":"M = stratified_split(       factor_name = \"V1\",       p_train = 0.75)  D = iris_DatasetExperiment() M = stratified_split(p_train=0.75,factor_name='Species') M = model_apply(M,D)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/structToolbox-package.html","id":null,"dir":"Reference","previous_headings":"","what":"structToolbox: Examples of tools built using the Statistics in R Using Class Templates (struct) package — structToolbox-package","title":"structToolbox: Examples of tools built using the Statistics in R Using Class Templates (struct) package — structToolbox-package","text":"package extends classes defined struct package","code":""},{"path":[]},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/structToolbox-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"structToolbox: Examples of tools built using the Statistics in R Using Class Templates (struct) package — structToolbox-package","text":"Maintainer: Gavin Rhys Lloyd g.r.lloyd@bham.ac.uk (ORCID) Authors: Ralf Johannes Maria Weber r.j.weber@bham.ac.uk","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/svm_plot_2d.html","id":null,"dir":"Reference","previous_headings":"","what":"SVM scatter plot — svm_plot_2d","title":"SVM scatter plot — svm_plot_2d","text":"scatter plot input data group calculated boundary SVM model.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/svm_plot_2d.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SVM scatter plot — svm_plot_2d","text":"","code":"svm_plot_2d(factor_name, npoints = 100, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/svm_plot_2d.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"SVM scatter plot — svm_plot_2d","text":"factor_name (character) name sample-meta column use. npoints (numeric) number grid points used plot boundary. default 100. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/svm_plot_2d.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"SVM scatter plot — svm_plot_2d","text":"  svm_plot_2d  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/svm_plot_2d.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"SVM scatter plot — svm_plot_2d","text":"object makes use functionality following packages: e1071","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/svm_plot_2d.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"SVM scatter plot — svm_plot_2d","text":"svm_plot_2d object inherits following struct classes: [svm_plot_2d] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/svm_plot_2d.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"SVM scatter plot — svm_plot_2d","text":"Meyer D, Dimitriadou E, Hornik K, Weingessel , Leisch F (2023). e1071: Misc Functions Department Statistics, Probability Theory Group (Formerly: E1071), TU Wien. R package version 1.7-14, https://CRAN.R-project.org/package=e1071.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/svm_plot_2d.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"SVM scatter plot — svm_plot_2d","text":"","code":"M = svm_plot_2d(       factor_name = \"V1\",       npoints = 100)  D = iris_DatasetExperiment() M = filter_smeta(mode='exclude',levels='setosa',factor_name='Species') +     mean_centre()+PCA(number_components=2)+     SVM(factor_name='Species',kernel='linear') M = model_apply(M,D)  C = svm_plot_2d(factor_name='Species') chart_plot(C,M[4],predicted(M[3]))"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE.html","id":null,"dir":"Reference","previous_headings":"","what":"tSNE — tSNE","title":"tSNE — tSNE","text":"t-Distributed Stochastic Neighbor Embedding.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"tSNE — tSNE","text":"","code":"tSNE(   dims = 2,   perplexity = 30,   max_iter = 100,   theta = 0.5,   check_duplicates = FALSE,   init = NULL,   eta = 200,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"tSNE — tSNE","text":"dims (numeric) number tSNE dimensions computed. default 2. perplexity (numeric) Perplexity parameter. default 30. max_iter (numeric) maximum number tSNE iterations. default 100. theta (numeric) Speed/accuracy trade-. value 0 gives exact tSNE. default 0.5. check_duplicates (logical) Check duplicates. Allowed values limited following: \"TRUE\": Checks presence exact duplicate samples. \"FALSE\": check exact duplicate samples. default FALSE. init (NULL, data.frame, DatasetExperiment) set coordinates initialising tSNE algorithm. NULL uses random initialisation. default NULL. eta (numeric) learning rate parameter. default 200. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"tSNE — tSNE","text":" tSNE object following output slots:","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"tSNE — tSNE","text":"object makes use functionality following packages: Rtsne","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"tSNE — tSNE","text":"tSNE object inherits following struct classes: [tSNE] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"tSNE — tSNE","text":"Krijthe JH (2015). Rtsne: T-Distributed Stochastic Neighbor Embedding using Barnes-Hut Implementation. R package version 0.17, https://github.com/jkrijthe/Rtsne. van der Maaten L, Hinton G (2008). \"Visualizing High-Dimensional Data Using t-SNE.\" Journal Machine Learning Research, 9, 2579-2605. van der Maaten L (2014). \"Accelerating t-SNE using Tree-Based Algorithms.\" Journal Machine Learning Research, 15, 3221-3245.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"tSNE — tSNE","text":"","code":"M = tSNE(       dims = 2,       perplexity = 30,       max_iter = 1000,       theta = 0.5,       check_duplicates = FALSE,       init = NULL,       eta = 200)  M = tSNE()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE_scatter.html","id":null,"dir":"Reference","previous_headings":"","what":"Feature boxplot — tSNE_scatter","title":"Feature boxplot — tSNE_scatter","text":"plots new representation data applying tSNE.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE_scatter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Feature boxplot — tSNE_scatter","text":"","code":"tSNE_scatter(factor_name, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE_scatter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Feature boxplot — tSNE_scatter","text":"factor_name (character) name sample-meta column use. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE_scatter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Feature boxplot — tSNE_scatter","text":"  tSNE_scatter  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE_scatter.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Feature boxplot — tSNE_scatter","text":"object makes use functionality following packages: Rtsne","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE_scatter.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Feature boxplot — tSNE_scatter","text":"tSNE_scatter object inherits following struct classes: [tSNE_scatter] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE_scatter.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Feature boxplot — tSNE_scatter","text":"Krijthe JH (2015). Rtsne: T-Distributed Stochastic Neighbor Embedding using Barnes-Hut Implementation. R package version 0.17, https://github.com/jkrijthe/Rtsne. van der Maaten L, Hinton G (2008). \"Visualizing High-Dimensional Data Using t-SNE.\" Journal Machine Learning Research, 9, 2579-2605. van der Maaten L (2014). \"Accelerating t-SNE using Tree-Based Algorithms.\" Journal Machine Learning Research, 15, 3221-3245.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tSNE_scatter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Feature boxplot — tSNE_scatter","text":"","code":"M = tSNE_scatter(       factor_name = \"V1\")  M = tSNE_scatter(factor_name='Species')"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tic_chart.html","id":null,"dir":"Reference","previous_headings":"","what":"Total Ion Count chart. — tic_chart","title":"Total Ion Count chart. — tic_chart","text":"scatter plot Total Ion Count (sum sample) versus run order.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tic_chart.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Total Ion Count chart. — tic_chart","text":"","code":"tic_chart(run_order, factor_name, connected = FALSE, ...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tic_chart.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Total Ion Count chart. — tic_chart","text":"run_order (character) column name sample_meta indicating run order samples. factor_name (character) name sample-meta column use. connected (logical) Plot samples connected grey line. default FALSE. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/tic_chart.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Total Ion Count chart. — tic_chart","text":"  tic_chart  object. object output slots. 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Multiple-test corrected p-values used indicate significance computed difference features.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ttest.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"t-test — ttest","text":"","code":"ttest(   alpha = 0.05,   mtc = \"fdr\",   factor_names,   paired = FALSE,   paired_factor = character(0),   equal_variance = FALSE,   conf_level = 0.95,   control_group = NULL,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/ttest.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"t-test — ttest","text":"alpha (numeric) p-value cutoff determining significance. default 0.05. mtc (character) Multiple test correction method. Allowed values limited following: \"bonferroni\": Bonferroni correction p-values multiplied number comparisons. \"fdr\": Benjamini Hochberg False Discovery Rate correction. \"none\": correction. default \"fdr\". factor_names (character) name sample meta column(s) use. paired (logical) Apply paired t-test. default FALSE. paired_factor (character) factor name encodes sample id pairing. default character(0). equal_variance (logical) Equal variance. Allowed values limited following: \"TRUE\": variance group treated equal using pooled variance estimate variance. \"FALSE\": variance group assumed equal Welch (Satterthwaite) approximation used. default FALSE. conf_level (numeric) confidence level interval. default 0.95. control_group (character, NULL) level name group used second group (possible) computing t-statistics. ensures positive t-statistic corresponds increase compared control group. default NULL. ... 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Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/vec_norm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Vector normalisation — vec_norm","text":" vec_norm object following output slots: struct object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/vec_norm.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Vector normalisation — vec_norm","text":"vec_norm object inherits following struct classes: [vec_norm] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/vec_norm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Vector normalisation — vec_norm","text":"","code":"M = vec_norm()  M = vec_norm()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/wilcox_p_hist.html","id":null,"dir":"Reference","previous_headings":"","what":"Histogram of p values — wilcox_p_hist","title":"Histogram of p values — wilcox_p_hist","text":"histogram p values wilcoxon signed rank test","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/wilcox_p_hist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Histogram of p values — wilcox_p_hist","text":"","code":"wilcox_p_hist(...)"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/wilcox_p_hist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Histogram of p values — wilcox_p_hist","text":"... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/wilcox_p_hist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Histogram of p values — wilcox_p_hist","text":"  wilcox_p_hist  object. object output slots. See chart_plot struct package plot chart object.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/wilcox_p_hist.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"Histogram of p values — wilcox_p_hist","text":"wilcox_p_hist object inherits following struct classes: [wilcox_p_hist] >> [chart] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/wilcox_p_hist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Histogram of p values — wilcox_p_hist","text":"","code":"M = wilcox_p_hist()  M = wilcox_p_hist()"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/wilcox_test.html","id":null,"dir":"Reference","previous_headings":"","what":"wilcoxon signed rank test — wilcox_test","title":"wilcoxon signed rank test — wilcox_test","text":"Mann-Whitney-Wilcoxon signed rank test compares ,ranks values two groups. non-parametric equivalent t-test. Multiple test corrected p-values computed indicators significance variable/feature.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/wilcox_test.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"wilcoxon signed rank test — wilcox_test","text":"","code":"wilcox_test(   alpha = 0.05,   mtc = \"fdr\",   factor_names,   paired = FALSE,   paired_factor = character(0),   conf_level = 0.95,   ... )"},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/wilcox_test.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"wilcoxon signed rank test — wilcox_test","text":"alpha (numeric) p-value cutoff determining significance. default 0.05. mtc (character) Multiple test correction method. Allowed values limited following: \"bonferroni\": Bonferroni correction p-values multiplied number comparisons. \"fdr\": Benjamini Hochberg False Discovery Rate correction. \"none\": correction. default \"fdr\". factor_names (character) name sample-meta column use. paired (logical) Apply paired test. default FALSE. paired_factor (character) factor name containing sample ids paired data. default character(0). conf_level (numeric) confidence level interval. default 0.95. ... Additional slots values passed struct_class.","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/wilcox_test.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"wilcoxon signed rank test — wilcox_test","text":" wilcox_test object following output slots: struct object","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/wilcox_test.html","id":"inheritance","dir":"Reference","previous_headings":"","what":"Inheritance","title":"wilcoxon signed rank test — wilcox_test","text":"wilcox_test object inherits following struct classes: [wilcox_test] >> [model] >> [struct_class]","code":""},{"path":"https://computational-metabolomics.github.io/structToolbox/reference/wilcox_test.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"wilcoxon signed rank test — wilcox_test","text":"","code":"M = wilcox_test(       alpha = 0.05,       mtc = \"fdr\",       factor_names = \"V1\",       paired = FALSE,       paired_factor = character(0),       conf_level = 0.95)  M = wilcox_test(factor_name='Class')"}]
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index f071a9b..52b1658 100644
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