update ref urls

paper/paper.bib

@@ -112,7 +112,7 @@ @Article{tmb
     number = {5},
     pages = {1--21},
     url = {http://www.jstatsoft.org/v070/i05/},
-    doi = {10.18637/jss.v079.i05}
+    doi = {10.18637/jss.v070.i05}
 }
 
 @article{thorley2018ssdtools,
@@ -163,8 +163,7 @@ @techreport{fox_methodologies_2021
 	institution = {Environmetrics Australia and Australian Institute of Marine Science},
 	author = {Fox, DR and Fisher, R and Thorley, JL and Schwarz, C},
 	month = mar,
-	year = {2021},
-  url={https://environmetrics.net/docs/FOX%20and%20FISHER%20Final_final_report_rev2.3.pdf?189db0&189db0}
+	year = {2021}
 }
 
 @techreport{fox_methodologies_2024,
@@ -173,6 +172,7 @@ @techreport{fox_methodologies_2024
 Report prepared for the Department of Climate Change, Energy, the Environment and Water},
 	institution = {Environmetrics Australia and Australian Institute of Marine Science},
 	author = {Fox, DR and Fisher, R and Thorley, JL},
+	doi = {10.25845/xtvt-yc51},
 	month = April,
 	year = {2024}
 }
@@ -192,7 +192,7 @@ @article{lepper2005manual
   volume={15},
   pages={51--52},
   year={2005},
-  url = {https://www.helpdeskwater.nl/publish/pages/131519/_16__manual_on_the_methodological_framework_to_derive_environmental_quality_standards_for_priori.pdf}
+  url = {https://www.wrrl-info.de/docs/manual-derivation-qs.pdf}
 }
 
 @techreport{bcmecc2019,

paper/paper.md

@@ -99,21 +99,21 @@ Finding the solution to this last equation is referred to as *finding the root(s
 
 `ssdtools` generates confidence intervals for $\text{HC}_x$ and $\text{HP}_u$ values via bootstrapping.
 By default all versions of `ssdtools` use parametric bootstrapping as it has better coverage than the equivalent non parametric approach used in other SSD modelling software such as `Burrlioz` [see @fox_methodologies_2021].
-The first two versions of `ssdtools` both calculated the model averaged CI from the weighted arithmetic mean of the CIs of the individual distributions.
+The first two versions of `ssdtools` both calculated the model averaged CI from the weighted arithmetic mean of the CIs of the individual distributions (`weighted_arithmetic`).
 Unfortunately, this approach has recently been shown to have poor coverage [@fox_methodologies_2024] and is inconsistent with the *inversion principle*.
 
 Consequently, v2 also offers a parametric bootstrap method that uses the joint cdf to generate data before refitting the original distribution set and solving for the newly estimated joint cdf [see details in @fox_methodologies_2024].
-This "multi" method can be implemented with and without re-estimation of the model weights.
+This "multi" method can be implemented with (`multi_free`) and without (`multi_fixed`) re-estimation of the model weights.
 However, although the "multi" method has good coverage it is computationally slow.
-As a result, the default method provided by the current update is a faster heuristic based on taking bootstrap samples from the individual distributions proportional to their weights [@fox_methodologies_2024].
+As a result, the default method (`weighted_samples`) provided by the current update is a faster heuristic based on taking bootstrap samples from the individual distributions proportional to their weights [@fox_methodologies_2024].
 
 ## Multiple Distribution Functions
 
 In order to implement the "multi" method of bootstrapping, v2 also provides the probability density (`ssd_pmulti()`), cumulative distribution (`ssd_qmulti()`) and random generation (`ssd_rmulti()`) functions for multiple distributions.
 
 ## Plotting 
 
-As well as to fitting SSDs and providing methods for estimating $\text{HC}_x$ and $\text{HP}_u$ values, `ssdtools` extends the `ggplot2` R package [@ggplot2] by defining `geom_ssdpoint()`, `geom_ssdsegment()`, `geom_hcintersect()` and `geom_xribbon()` geoms as well as a discrete color-blind scale `scale_color_sdd()` for SSD plots.
+As well as fitting SSDs and providing methods for estimating $\text{HC}_x$ and $\text{HP}_u$ values, `ssdtools` extends the `ggplot2` R package [@ggplot2] by defining `geom_ssdpoint()`, `geom_ssdsegment()`, `geom_hcintersect()` and `geom_xribbon()` geoms and a discrete color-blind scale `scale_color_sdd()` for SSD plots.
 
 # Example of use