diff --git a/.Rbuildignore b/.Rbuildignore
index 475a5231..ab82a293 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -52,3 +52,4 @@ references.bib
 ^revdep$
 ^CRAN-SUBMISSION$
 ^LICENSE\.md$
+^[.]?air[.]toml$
diff --git a/R/cor_lower.R b/R/cor_lower.R
index d1b9ecac..6a6a801e 100644
--- a/R/cor_lower.R
+++ b/R/cor_lower.R
@@ -45,7 +45,10 @@ cor_lower.easycorrelation <- function(x, diag = FALSE, ...) {
   for (param1 in rownames(m)) {
     for (param2 in colnames(m)) {
       if (tri[param1, param2]) {
-        tokeep <- c(tokeep, which(x$Parameter1 == param1 & x$Parameter2 == param2))
+        tokeep <- c(
+          tokeep,
+          which(x$Parameter1 == param1 & x$Parameter2 == param2)
+        )
       }
     }
   }
diff --git a/R/cor_smooth.R b/R/cor_smooth.R
index 0399de79..8e7cda3b 100644
--- a/R/cor_smooth.R
+++ b/R/cor_smooth.R
@@ -30,47 +30,66 @@ cor_smooth <- function(x, method = "psych", verbose = TRUE, ...) {
 
 
 #' @export
-cor_smooth.easycorrelation <- function(x,
-                                       method = "psych",
-                                       verbose = TRUE,
-                                       tol = 10^-12,
-                                       ...) {
-  m <- cor_smooth(as.matrix(x), method = method, verbose = verbose, tol = tol, ...)
+cor_smooth.easycorrelation <- function(
+  x,
+  method = "psych",
+  verbose = TRUE,
+  tol = 10^-12,
+  ...
+) {
+  m <- cor_smooth(
+    as.matrix(x),
+    method = method,
+    verbose = verbose,
+    tol = tol,
+    ...
+  )
 
   if (isTRUE(attributes(m)$smoothed)) {
     estim <- names(x)[names(x) %in% c("r", "rho", "tau", "D")][1]
 
     for (param1 in row.names(m)) {
       for (param2 in colnames(m)) {
-        if (nrow(x[x$Parameter1 == param1 & x$Parameter2 == param2, ]) == 0) next
+        if (nrow(x[x$Parameter1 == param1 & x$Parameter2 == param2, ]) == 0) {
+          next
+        }
         # Print changes
         if (verbose) {
           val1 <- x[x$Parameter1 == param1 & x$Parameter2 == param2, estim]
           val2 <- m[param1, param2]
           if (round(val1 - val2, digits = 2) == 0) {
-            insight::print_color(paste0(
-              param1,
-              " - ",
-              param2,
-              ": no change (",
-              insight::format_value(val1),
-              ")\n"
-            ), "green")
+            insight::print_color(
+              paste0(
+                param1,
+                " - ",
+                param2,
+                ": no change (",
+                insight::format_value(val1),
+                ")\n"
+              ),
+              "green"
+            )
           } else {
-            insight::print_color(paste0(
-              param1,
-              " - ",
-              param2,
-              ": ",
-              insight::format_value(val1),
-              " -> ",
-              insight::format_value(val2),
-              "\n"
-            ), "red")
+            insight::print_color(
+              paste0(
+                param1,
+                " - ",
+                param2,
+                ": ",
+                insight::format_value(val1),
+                " -> ",
+                insight::format_value(val2),
+                "\n"
+              ),
+              "red"
+            )
           }
           cat("\n")
         }
-        x[x$Parameter1 == param1 & x$Parameter2 == param2, estim] <- m[param1, param2]
+        x[x$Parameter1 == param1 & x$Parameter2 == param2, estim] <- m[
+          param1,
+          param2
+        ]
       }
     }
 
@@ -87,16 +106,20 @@ cor_smooth.easycorrelation <- function(x,
 
 
 #' @export
-cor_smooth.matrix <- function(x,
-                              method = "psych",
-                              verbose = TRUE,
-                              tol = 10^-12,
-                              ...) {
+cor_smooth.matrix <- function(
+  x,
+  method = "psych",
+  verbose = TRUE,
+  tol = 10^-12,
+  ...
+) {
   method <- match.arg(method, choices = c("psych", "hj", "lrs"))
 
   # Already positive definite
   if (is.positive_definite(x, tol = tol, ...)) {
-    if (verbose) message("Matrix is positive definite, smoothing was not needed.")
+    if (verbose) {
+      message("Matrix is positive definite, smoothing was not needed.")
+    }
     return(x)
   }
 
@@ -104,7 +127,10 @@ cor_smooth.matrix <- function(x,
     insight::check_if_installed("psych")
     x <- suppressWarnings(psych::cor.smooth(x, eig.tol = tol, ...))
   } else {
-    out <- try(suppressMessages(mbend::bend(x, method = method, ...)), silent = TRUE)
+    out <- try(
+      suppressMessages(mbend::bend(x, method = method, ...)),
+      silent = TRUE
+    )
     if (inherits(out, "try-error")) {
       return(x)
     }
@@ -134,9 +160,12 @@ is.positive_definite.matrix <- function(x, tol = 10^-12, ...) {
 
   # validation checks
   if (inherits(eigens, "try-error")) {
-    stop(insight::format_message(
-      "There is something seriously wrong with the correlation matrix, as some of the eigen values are NA."
-    ), call. = FALSE)
+    stop(
+      insight::format_message(
+        "There is something seriously wrong with the correlation matrix, as some of the eigen values are NA."
+      ),
+      call. = FALSE
+    )
   }
 
   # Find out
diff --git a/R/cor_sort.R b/R/cor_sort.R
index 554464e6..d0ef8471 100644
--- a/R/cor_sort.R
+++ b/R/cor_sort.R
@@ -20,13 +20,28 @@
 #' cor_sort(x, hclust_method = "ward.D2") # It can also reorder the long form output
 #' cor_sort(summary(x, redundant = TRUE)) # As well as from the summary
 #' @export
-cor_sort <- function(x, distance = "correlation", hclust_method = "complete", ...) {
+cor_sort <- function(
+  x,
+  distance = "correlation",
+  hclust_method = "complete",
+  ...
+) {
   UseMethod("cor_sort")
 }
 
 #' @export
-cor_sort.easycorrelation <- function(x, distance = "correlation", hclust_method = "complete", ...) {
-  m <- cor_sort(as.matrix(x), distance = distance, hclust_method = hclust_method, ...)
+cor_sort.easycorrelation <- function(
+  x,
+  distance = "correlation",
+  hclust_method = "complete",
+  ...
+) {
+  m <- cor_sort(
+    as.matrix(x),
+    distance = distance,
+    hclust_method = hclust_method,
+    ...
+  )
   x$Parameter1 <- factor(x$Parameter1, levels = rownames(m))
   x$Parameter2 <- factor(x$Parameter2, levels = colnames(m))
   reordered <- x[order(x$Parameter1, x$Parameter2), ]
@@ -48,7 +63,12 @@ cor_sort.easycorrelation <- function(x, distance = "correlation", hclust_method
 
 
 #' @export
-cor_sort.easycormatrix <- function(x, distance = "correlation", hclust_method = "complete", ...) {
+cor_sort.easycormatrix <- function(
+  x,
+  distance = "correlation",
+  hclust_method = "complete",
+  ...
+) {
   if (!"Parameter" %in% colnames(x)) {
     return(NextMethod())
   }
@@ -60,7 +80,9 @@ cor_sort.easycormatrix <- function(x, distance = "correlation", hclust_method =
 
   # If non-redundant matrix, fail (## TODO: fix that)
   if (anyNA(m)) {
-    insight::format_error("Non-redundant matrices are not supported yet. Try again by setting summary(..., redundant = TRUE)")
+    insight::format_error(
+      "Non-redundant matrices are not supported yet. Try again by setting summary(..., redundant = TRUE)"
+    )
   }
 
   # Get sorted matrix
@@ -77,9 +99,22 @@ cor_sort.easycormatrix <- function(x, distance = "correlation", hclust_method =
   )
 
   # Reorder attributes (p-values) etc.
-  for (id in c("p", "CI", "CI_low", "CI_high", "BF", "Method", "n_Obs", "df_error", "t")) {
+  for (id in c(
+    "p",
+    "CI",
+    "CI_low",
+    "CI_high",
+    "BF",
+    "Method",
+    "n_Obs",
+    "df_error",
+    "t"
+  )) {
     if (id %in% names(attributes(reordered))) {
-      attributes(reordered)[[id]] <- attributes(reordered)[[id]][order(x$Parameter), names(reordered)]
+      attributes(reordered)[[id]] <- attributes(reordered)[[id]][
+        order(x$Parameter),
+        names(reordered)
+      ]
     }
   }
 
@@ -91,9 +126,19 @@ cor_sort.easycormatrix <- function(x, distance = "correlation", hclust_method =
 
 
 #' @export
-cor_sort.matrix <- function(x, distance = "correlation", hclust_method = "complete", ...) {
+cor_sort.matrix <- function(
+  x,
+  distance = "correlation",
+  hclust_method = "complete",
+  ...
+) {
   if (isSquare(x) && all(colnames(x) %in% rownames(x))) {
-    i <- .cor_sort_square(x, distance = distance, hclust_method = hclust_method, ...)
+    i <- .cor_sort_square(
+      x,
+      distance = distance,
+      hclust_method = hclust_method,
+      ...
+    )
   } else {
     i <- .cor_sort_nonsquare(x, distance = "euclidean", ...)
   }
@@ -111,8 +156,12 @@ cor_sort.matrix <- function(x, distance = "correlation", hclust_method = "comple
 
 # Utils -------------------------------------------------------------------
 
-
-.cor_sort_square <- function(m, distance = "correlation", hclust_method = "complete", ...) {
+.cor_sort_square <- function(
+  m,
+  distance = "correlation",
+  hclust_method = "complete",
+  ...
+) {
   if (distance == "correlation") {
     d <- stats::as.dist((1 - m) / 2) # r = -1 -> d = 1; r = 1 -> d = 0
   } else if (distance == "raw") {
diff --git a/R/cor_test.R b/R/cor_test.R
index 1eae95ad..2f53914c 100644
--- a/R/cor_test.R
+++ b/R/cor_test.R
@@ -118,38 +118,53 @@
 #' }
 #' }
 #' @export
-cor_test <- function(data,
-                     x,
-                     y,
-                     method = "pearson",
-                     ci = 0.95,
-                     bayesian = FALSE,
-                     bayesian_prior = "medium",
-                     bayesian_ci_method = "hdi",
-                     bayesian_test = c("pd", "rope", "bf"),
-                     include_factors = FALSE,
-                     partial = FALSE,
-                     partial_bayesian = FALSE,
-                     multilevel = FALSE,
-                     ranktransform = FALSE,
-                     winsorize = FALSE,
-                     verbose = TRUE,
-                     ...) {
+cor_test <- function(
+  data,
+  x,
+  y,
+  method = "pearson",
+  ci = 0.95,
+  bayesian = FALSE,
+  bayesian_prior = "medium",
+  bayesian_ci_method = "hdi",
+  bayesian_test = c("pd", "rope", "bf"),
+  include_factors = FALSE,
+  partial = FALSE,
+  partial_bayesian = FALSE,
+  multilevel = FALSE,
+  ranktransform = FALSE,
+  winsorize = FALSE,
+  verbose = TRUE,
+  ...
+) {
   # valid matrix checks
   if (!all(x %in% names(data)) || !all(y %in% names(data))) {
-    insight::format_error("The names you entered for x and y are not available in the dataset. Make sure there are no typos!")
+    insight::format_error(
+      "The names you entered for x and y are not available in the dataset. Make sure there are no typos!"
+    )
   }
 
-  if (ci == "default") ci <- 0.95
-  if (!partial && (partial_bayesian || multilevel)) partial <- TRUE
+  if (ci == "default") {
+    ci <- 0.95
+  }
+  if (!partial && (partial_bayesian || multilevel)) {
+    partial <- TRUE
+  }
 
   # Make sure factor is no factor
   if (!method %in% c("tetra", "tetrachoric", "poly", "polychoric")) {
-    data[c(x, y)] <- datawizard::to_numeric(data[c(x, y)], dummy_factors = FALSE)
+    data[c(x, y)] <- datawizard::to_numeric(
+      data[c(x, y)],
+      dummy_factors = FALSE
+    )
   }
 
   # However, for poly, we need factors!
-  if (method %in% c("poly", "polychoric") && all(vapply(data[c(x, y)], is.numeric, FUN.VALUE = TRUE))) {
+  if (
+    method %in%
+      c("poly", "polychoric") &&
+      all(vapply(data[c(x, y)], is.numeric, FUN.VALUE = TRUE))
+  ) {
     # convert all input to factors, but only if all input currently is numeric
     # we allow mix of numeric and factors
     data[c(x, y)] <- datawizard::to_factor(data[c(x, y)])
@@ -159,13 +174,23 @@ cor_test <- function(data,
   if (!isFALSE(partial)) {
     # partial
     if (isTRUE(partial)) {
-      data[[x]] <- datawizard::adjust(data[names(data) != y], multilevel = multilevel, bayesian = partial_bayesian)[[x]]
-      data[[y]] <- datawizard::adjust(data[names(data) != x], multilevel = multilevel, bayesian = partial_bayesian)[[y]]
+      data[[x]] <- datawizard::adjust(
+        data[names(data) != y],
+        multilevel = multilevel,
+        bayesian = partial_bayesian
+      )[[x]]
+      data[[y]] <- datawizard::adjust(
+        data[names(data) != x],
+        multilevel = multilevel,
+        bayesian = partial_bayesian
+      )[[y]]
     }
 
     # semi-partial
     if (partial == "semi") {
-      insight::format_error("Semi-partial correlations are not supported yet. Get in touch if you want to contribute.")
+      insight::format_error(
+        "Semi-partial correlations are not supported yet. Get in touch if you want to contribute."
+      )
     }
   }
 
@@ -178,7 +203,8 @@ cor_test <- function(data,
 
     # winsorization would otherwise fail in case of NAs present
     data <- as.data.frame(
-      datawizard::winsorize(stats::na.omit(data[c(x, y)]),
+      datawizard::winsorize(
+        stats::na.omit(data[c(x, y)]),
         threshold = winsorize,
         verbose = verbose
       )
@@ -187,7 +213,11 @@ cor_test <- function(data,
 
   # Rank transform (i.e., "robust")
   if (ranktransform) {
-    data[c(x, y)] <- datawizard::ranktransform(data[c(x, y)], sign = FALSE, method = "average")
+    data[c(x, y)] <- datawizard::ranktransform(
+      data[c(x, y)],
+      sign = FALSE,
+      method = "average"
+    )
   }
 
   # check if enough no. of obs ------------------------------
@@ -197,7 +227,12 @@ cor_test <- function(data,
   invalid <- FALSE
   if (n_obs < 3L) {
     if (isTRUE(verbose)) {
-      insight::format_warning(paste(x, "and", y, "have less than 3 complete observations. Returning NA."))
+      insight::format_warning(paste(
+        x,
+        "and",
+        y,
+        "have less than 3 complete observations. Returning NA."
+      ))
     }
     invalid <- TRUE
     original_info <- list(data = data, x = x, y = y)
@@ -206,11 +241,14 @@ cor_test <- function(data,
     y <- "disp"
   }
 
-
   # Find method
   method <- tolower(method)
-  if (method == "auto" && !bayesian) method <- .find_correlationtype(data, x, y)
-  if (method == "auto" && bayesian) method <- "pearson"
+  if (method == "auto" && !bayesian) {
+    method <- .find_correlationtype(data, x, y)
+  }
+  if (method == "auto" && bayesian) {
+    method <- "pearson"
+  }
 
   # Frequentist
   if (!bayesian) {
@@ -224,7 +262,9 @@ cor_test <- function(data,
       out <- .cor_test_biweight(data, x, y, ci = ci, ...)
     } else if (method == "distance") {
       out <- .cor_test_distance(data, x, y, ci = ci, ...)
-    } else if (method %in% c("percentage", "percentage_bend", "percentagebend", "pb")) {
+    } else if (
+      method %in% c("percentage", "percentage_bend", "percentagebend", "pb")
+    ) {
       out <- .cor_test_percentage(data, x, y, ci = ci, ...)
     } else if (method %in% c("blomqvist", "median", "medial")) {
       out <- .cor_test_blomqvist(data, x, y, ci = ci, ...)
@@ -244,23 +284,43 @@ cor_test <- function(data,
 
     # Bayesian
   } else if (method %in% c("tetra", "tetrachoric")) {
-    insight::format_error("Tetrachoric Bayesian correlations are not supported yet. Get in touch if you want to contribute.")
+    insight::format_error(
+      "Tetrachoric Bayesian correlations are not supported yet. Get in touch if you want to contribute."
+    )
   } else if (method %in% c("poly", "polychoric")) {
-    insight::format_error("Polychoric Bayesian correlations are not supported yet. Get in touch if you want to contribute.")
+    insight::format_error(
+      "Polychoric Bayesian correlations are not supported yet. Get in touch if you want to contribute."
+    )
   } else if (method %in% c("biserial", "pointbiserial", "point-biserial")) {
-    insight::format_error("Biserial Bayesian correlations are not supported yet. Get in touch if you want to contribute.")
+    insight::format_error(
+      "Biserial Bayesian correlations are not supported yet. Get in touch if you want to contribute."
+    )
   } else if (method == "biweight") {
-    insight::format_error("Biweight Bayesian correlations are not supported yet. Get in touch if you want to contribute.")
+    insight::format_error(
+      "Biweight Bayesian correlations are not supported yet. Get in touch if you want to contribute."
+    )
   } else if (method == "distance") {
-    insight::format_error("Bayesian distance correlations are not supported yet. Get in touch if you want to contribute.")
-  } else if (method %in% c("percentage", "percentage_bend", "percentagebend", "pb")) {
-    insight::format_error("Bayesian Percentage Bend correlations are not supported yet. Get in touch if you want to contribute.")
+    insight::format_error(
+      "Bayesian distance correlations are not supported yet. Get in touch if you want to contribute."
+    )
+  } else if (
+    method %in% c("percentage", "percentage_bend", "percentagebend", "pb")
+  ) {
+    insight::format_error(
+      "Bayesian Percentage Bend correlations are not supported yet. Get in touch if you want to contribute."
+    )
   } else if (method %in% c("blomqvist", "median", "medial")) {
-    insight::format_error("Bayesian Blomqvist correlations are not supported yet. Check-out the BBcor package (https://github.com/donaldRwilliams/BBcor).")
+    insight::format_error(
+      "Bayesian Blomqvist correlations are not supported yet. Check-out the BBcor package (https://github.com/donaldRwilliams/BBcor)."
+    )
   } else if (method == "hoeffding") {
-    insight::format_error("Bayesian Hoeffding's correlations are not supported yet. Check-out the BBcor package (https://github.com/donaldRwilliams/BBcor).")
+    insight::format_error(
+      "Bayesian Hoeffding's correlations are not supported yet. Check-out the BBcor package (https://github.com/donaldRwilliams/BBcor)."
+    )
   } else if (method == "gamma") {
-    insight::format_error("Bayesian gamma correlations are not supported yet. Get in touch if you want to contribute.")
+    insight::format_error(
+      "Bayesian gamma correlations are not supported yet. Get in touch if you want to contribute."
+    )
   } else if (method %in% c("shepherd", "sheperd", "shepherdspi", "pi")) {
     out <- .cor_test_shepherd(data, x, y, ci = ci, bayesian = TRUE, ...)
   } else {
@@ -291,7 +351,17 @@ cor_test <- function(data,
 
   # Reorder columns
   if ("CI_low" %in% names(out)) {
-    col_order <- c("Parameter1", "Parameter2", "r", "rho", "tau", "Dxy", "CI", "CI_low", "CI_high")
+    col_order <- c(
+      "Parameter1",
+      "Parameter2",
+      "r",
+      "rho",
+      "tau",
+      "Dxy",
+      "CI",
+      "CI_low",
+      "CI_high"
+    )
     out <- out[c(
       col_order[col_order %in% names(out)],
       setdiff(colnames(out), col_order[col_order %in% names(out)])
@@ -299,17 +369,23 @@ cor_test <- function(data,
   }
 
   # Output
-  attr(out, "coefficient_name") <- c("rho", "r", "tau", "Dxy")[c("rho", "r", "tau", "Dxy") %in% names(out)][1]
+  attr(out, "coefficient_name") <- c("rho", "r", "tau", "Dxy")[
+    c("rho", "r", "tau", "Dxy") %in% names(out)
+  ][1]
   attr(out, "ci") <- ci
   attr(out, "data") <- data
-  class(out) <- unique(c("easycor_test", "easycorrelation", "parameters_model", class(out)))
+  class(out) <- unique(c(
+    "easycor_test",
+    "easycorrelation",
+    "parameters_model",
+    class(out)
+  ))
   out
 }
 
 
 # Utilities ---------------------------------------------------------------
 
-
 #' @keywords internal
 .complete_variable_x <- function(data, x, y) {
   data[[x]][stats::complete.cases(data[[x]], data[[y]])]
diff --git a/R/cor_test_bayes.R b/R/cor_test_bayes.R
index e9acaa5c..e45430fc 100644
--- a/R/cor_test_bayes.R
+++ b/R/cor_test_bayes.R
@@ -1,13 +1,15 @@
 #' @keywords internal
-.cor_test_bayes <- function(data,
-                            x,
-                            y,
-                            ci = 0.95,
-                            method = "pearson",
-                            bayesian_prior = "medium",
-                            bayesian_ci_method = "hdi",
-                            bayesian_test = c("pd", "rope", "bf"),
-                            ...) {
+.cor_test_bayes <- function(
+  data,
+  x,
+  y,
+  ci = 0.95,
+  method = "pearson",
+  bayesian_prior = "medium",
+  bayesian_ci_method = "hdi",
+  bayesian_test = c("pd", "rope", "bf"),
+  ...
+) {
   insight::check_if_installed("BayesFactor")
 
   var_x <- .complete_variable_x(data, x, y)
@@ -44,16 +46,18 @@
 
 
 #' @keywords internal
-.cor_test_bayes_base <- function(x,
-                                 y,
-                                 var_x,
-                                 var_y,
-                                 ci = 0.95,
-                                 bayesian_prior = "medium",
-                                 bayesian_ci_method = "hdi",
-                                 bayesian_test = c("pd", "rope", "bf"),
-                                 method = "pearson",
-                                 ...) {
+.cor_test_bayes_base <- function(
+  x,
+  y,
+  var_x,
+  var_y,
+  ci = 0.95,
+  bayesian_prior = "medium",
+  bayesian_ci_method = "hdi",
+  bayesian_test = c("pd", "rope", "bf"),
+  method = "pearson",
+  ...
+) {
   insight::check_if_installed("BayesFactor")
 
   if (x == y) {
@@ -72,15 +76,33 @@
       rope_ci = 1,
       ...
     )
-    if ("Median" %in% names(params)) params$Median <- 1
-    if ("Mean" %in% names(params)) params$Mean <- 1
-    if ("MAP" %in% names(params)) params$MAP <- 1
-    if ("SD" %in% names(params)) params$SD <- 0
-    if ("MAD" %in% names(params)) params$MAD <- 0
-    if ("CI_low" %in% names(params)) params$CI_low <- 1
-    if ("CI_high" %in% names(params)) params$CI_high <- 1
-    if ("pd" %in% names(params)) params$pd <- 1
-    if ("ROPE_Percentage" %in% names(params)) params$ROPE_Percentage <- 0
+    if ("Median" %in% names(params)) {
+      params$Median <- 1
+    }
+    if ("Mean" %in% names(params)) {
+      params$Mean <- 1
+    }
+    if ("MAP" %in% names(params)) {
+      params$MAP <- 1
+    }
+    if ("SD" %in% names(params)) {
+      params$SD <- 0
+    }
+    if ("MAD" %in% names(params)) {
+      params$MAD <- 0
+    }
+    if ("CI_low" %in% names(params)) {
+      params$CI_low <- 1
+    }
+    if ("CI_high" %in% names(params)) {
+      params$CI_high <- 1
+    }
+    if ("pd" %in% names(params)) {
+      params$pd <- 1
+    }
+    if ("ROPE_Percentage" %in% names(params)) {
+      params$ROPE_Percentage <- 0
+    }
     if ("BF" %in% names(params)) params$BF <- Inf
   } else {
     rez <- suppressWarnings(BayesFactor::correlationBF(
diff --git a/R/cor_test_biserial.R b/R/cor_test_biserial.R
index 0a874205..5d97bb7b 100644
--- a/R/cor_test_biserial.R
+++ b/R/cor_test_biserial.R
@@ -1,5 +1,12 @@
 #' @keywords internal
-.cor_test_biserial <- function(data, x, y, ci = 0.95, method = "biserial", ...) {
+.cor_test_biserial <- function(
+  data,
+  x,
+  y,
+  ci = 0.95,
+  method = "biserial",
+  ...
+) {
   # valid matrix
   if (.vartype(data[[x]])$is_binary && !.vartype(data[[y]])$is_binary) {
     binary <- x
@@ -14,7 +21,9 @@
   }
 
   # Rescale to 0-1
-  if (.vartype(data[[binary]])$is_factor || .vartype(data[[binary]])$is_character) {
+  if (
+    .vartype(data[[binary]])$is_factor || .vartype(data[[binary]])$is_character
+  ) {
     data[[binary]] <- as.numeric(as.factor(data[[binary]]))
   }
 
@@ -27,7 +36,15 @@
   if (method == "biserial") {
     out <- .cor_test_biserial_biserial(data, x, y, continuous, binary, ci)
   } else {
-    out <- .cor_test_biserial_pointbiserial(data, x, y, continuous, binary, ci, ...)
+    out <- .cor_test_biserial_pointbiserial(
+      data,
+      x,
+      y,
+      continuous,
+      binary,
+      ci,
+      ...
+    )
   }
 
   out
@@ -35,8 +52,23 @@
 
 
 #' @keywords internal
-.cor_test_biserial_pointbiserial <- function(data, x, y, continuous, binary, ci, ...) {
-  out <- .cor_test_freq(data, continuous, binary, ci = ci, method = "pearson", ...)
+.cor_test_biserial_pointbiserial <- function(
+  data,
+  x,
+  y,
+  continuous,
+  binary,
+  ci,
+  ...
+) {
+  out <- .cor_test_freq(
+    data,
+    continuous,
+    binary,
+    ci = ci,
+    method = "pearson",
+    ...
+  )
   names(out)[names(out) == "r"] <- "rho"
   out$Parameter1 <- x
   out$Parameter2 <- y
@@ -51,7 +83,6 @@
   var_x <- .complete_variable_x(data, continuous, binary)
   var_y <- .complete_variable_y(data, continuous, binary)
 
-
   m1 <- mean(var_x[var_y == 1])
   m0 <- mean(var_x[var_y == 0])
   quan <- mean(var_y)
diff --git a/R/cor_test_biweight.R b/R/cor_test_biweight.R
index c2f18ef5..e85fb415 100644
--- a/R/cor_test_biweight.R
+++ b/R/cor_test_biweight.R
@@ -3,7 +3,6 @@
   var_x <- .complete_variable_x(data, x, y)
   var_y <- .complete_variable_y(data, x, y)
 
-
   # https://github.com/easystats/correlation/issues/13
   u <- (var_x - stats::median(var_x)) / (9 * stats::mad(var_x, constant = 1))
   v <- (var_y - stats::median(var_y)) / (9 * stats::mad(var_y, constant = 1))
@@ -14,7 +13,6 @@
   w_x <- I_x * (1 - u^2)^2
   w_y <- I_y * (1 - v^2)^2
 
-
   denominator_x <- sqrt(sum(((var_x - stats::median(var_x)) * w_x)^2))
   x_curly <- ((var_x - stats::median(var_x)) * w_x) / denominator_x
 
diff --git a/R/cor_test_distance.R b/R/cor_test_distance.R
index 88e8ba25..41d7080a 100644
--- a/R/cor_test_distance.R
+++ b/R/cor_test_distance.R
@@ -39,7 +39,6 @@
 
 # Basis -------------------------------------------------------------------
 
-
 #' @keywords internal
 .cor_test_distance_corrected <- function(x, y, ci = 0.95) {
   x <- as.matrix(stats::dist(x))
@@ -102,7 +101,6 @@
 
 # Utils -------------------------------------------------------------------
 
-
 #' @keywords internal
 .A_kl <- function(x, index) {
   d <- as.matrix(x)^index
@@ -121,7 +119,9 @@
   ## denoted A* (or B*) in JMVA t-test paper (2013)
   d <- as.matrix(d)
   n <- nrow(d)
-  if (n != ncol(d)) stop("Argument d should be distance", call. = FALSE)
+  if (n != ncol(d)) {
+    stop("Argument d should be distance", call. = FALSE)
+  }
   m <- rowMeans(d)
   M <- mean(d)
   a <- sweep(d, 1, m)
diff --git a/R/cor_test_freq.R b/R/cor_test_freq.R
index 68a00aa8..4a7da17f 100644
--- a/R/cor_test_freq.R
+++ b/R/cor_test_freq.R
@@ -8,9 +8,28 @@
 
 
 #' @keywords internal
-.cor_test_base <- function(x, y, var_x, var_y, ci = 0.95, method = "pearson", ...) {
-  method <- match.arg(tolower(method), c("pearson", "kendall", "spearman", "somers"), several.ok = FALSE)
-  rez <- stats::cor.test(var_x, var_y, conf.level = ci, method = method, exact = FALSE, ...)
+.cor_test_base <- function(
+  x,
+  y,
+  var_x,
+  var_y,
+  ci = 0.95,
+  method = "pearson",
+  ...
+) {
+  method <- match.arg(
+    tolower(method),
+    c("pearson", "kendall", "spearman", "somers"),
+    several.ok = FALSE
+  )
+  rez <- stats::cor.test(
+    var_x,
+    var_y,
+    conf.level = ci,
+    method = method,
+    exact = FALSE,
+    ...
+  )
 
   # params <- parameters::model_parameters(rez)
   # this doubles performance according to computation time
@@ -20,21 +39,35 @@
   params$Parameter2 <- y
 
   if (x == y) {
-    if ("t" %in% names(params)) params$t <- Inf
-    if ("z" %in% names(params)) params$z <- Inf
+    if ("t" %in% names(params)) {
+      params$t <- Inf
+    }
+    if ("z" %in% names(params)) {
+      params$z <- Inf
+    }
     if ("S" %in% names(params)) params$S <- Inf
   }
 
   # Add CI for non-pearson correlations
   if (method %in% c("kendall", "spearman")) {
-    rez_ci <- cor_to_ci(rez$estimate, n = length(var_x), ci = ci, method = method, ...)
+    rez_ci <- cor_to_ci(
+      rez$estimate,
+      n = length(var_x),
+      ci = ci,
+      method = method,
+      ...
+    )
     params$CI_low <- rez_ci$CI_low
     params$CI_high <- rez_ci$CI_high
   }
 
   # see ?cor.test: CI only in case of at least 4 complete pairs of observations
-  if (!("CI_low" %in% names(params))) params$CI_low <- NA
-  if (!("CI_high" %in% names(params))) params$CI_high <- NA
+  if (!("CI_low" %in% names(params))) {
+    params$CI_low <- NA
+  }
+  if (!("CI_high" %in% names(params))) {
+    params$CI_high <- NA
+  }
 
   params
 }
diff --git a/R/cor_test_gamma.R b/R/cor_test_gamma.R
index 31273ee5..ab3cfe61 100644
--- a/R/cor_test_gamma.R
+++ b/R/cor_test_gamma.R
@@ -13,7 +13,6 @@
   p <- cor_to_p(r, n = length(var_x))
   ci_vals <- cor_to_ci(r, n = length(var_x), ci = ci)
 
-
   data.frame(
     Parameter1 = x,
     Parameter2 = y,
diff --git a/R/cor_test_polychoric.R b/R/cor_test_polychoric.R
index 16411008..bb81540a 100644
--- a/R/cor_test_polychoric.R
+++ b/R/cor_test_polychoric.R
@@ -7,10 +7,11 @@
 
   # valid matrix check
   if (!is.factor(var_x) && !is.factor(var_y)) {
-    insight::format_error("Polychoric correlations can only be ran on ordinal factors.")
+    insight::format_error(
+      "Polychoric correlations can only be ran on ordinal factors."
+    )
   }
 
-
   if (!is.factor(var_x) || !is.factor(var_y)) {
     insight::check_if_installed("polycor", "for 'polyserial' correlations")
 
diff --git a/R/cor_test_shepherd.R b/R/cor_test_shepherd.R
index 363540af..736630c9 100644
--- a/R/cor_test_shepherd.R
+++ b/R/cor_test_shepherd.R
@@ -8,10 +8,20 @@
 
   if (bayesian) {
     data <- data[not_outliers, ]
-    data[c(x, y)] <- datawizard::ranktransform(data[c(x, y)], sign = TRUE, method = "average")
+    data[c(x, y)] <- datawizard::ranktransform(
+      data[c(x, y)],
+      sign = TRUE,
+      method = "average"
+    )
     out <- .cor_test_bayes(data, x, y, ci = ci)
   } else {
-    out <- .cor_test_freq(data[not_outliers, ], x, y, ci = ci, method = "spearman")
+    out <- .cor_test_freq(
+      data[not_outliers, ],
+      x,
+      y,
+      ci = ci,
+      method = "spearman"
+    )
   }
   out$Method <- "Shepherd's Pi"
   out
diff --git a/R/cor_test_tetrachoric.R b/R/cor_test_tetrachoric.R
index df776683..8429f9ef 100644
--- a/R/cor_test_tetrachoric.R
+++ b/R/cor_test_tetrachoric.R
@@ -7,7 +7,9 @@
 
   # valid matrix check
   if (length(unique(var_x)) > 2 && length(unique(var_y)) > 2) {
-    insight::format_error("Tetrachoric correlations can only be ran on dichotomous data.")
+    insight::format_error(
+      "Tetrachoric correlations can only be ran on dichotomous data."
+    )
   }
 
   # Reconstruct dataframe
diff --git a/R/cor_text.R b/R/cor_text.R
index 35cf269e..cdc56b28 100644
--- a/R/cor_text.R
+++ b/R/cor_text.R
@@ -16,11 +16,21 @@
 #'
 #' cor_text(rez)
 #' @export
-cor_text <- function(x, show_ci = TRUE, show_statistic = TRUE, show_sig = TRUE, ...) {
+cor_text <- function(
+  x,
+  show_ci = TRUE,
+  show_statistic = TRUE,
+  show_sig = TRUE,
+  ...
+) {
   # Estimate
   candidates <- c("rho", "r", "tau", "Difference", "r_rank_biserial")
   estimate <- candidates[candidates %in% names(x)][1]
-  out_text <- paste0(tolower(estimate), " = ", insight::format_value(x[[estimate]]))
+  out_text <- paste0(
+    tolower(estimate),
+    " = ",
+    insight::format_value(x[[estimate]])
+  )
 
   # CI
   if (show_ci && all(c("CI_high", "CI_low") %in% names(x))) {
@@ -29,7 +39,11 @@ cor_text <- function(x, show_ci = TRUE, show_statistic = TRUE, show_sig = TRUE,
       out_text <- paste0(
         out_text,
         ", ",
-        insight::format_ci(x$CI_low, x$CI_high, ci = attributes(x$conf.int)$conf.level)
+        insight::format_ci(
+          x$CI_low,
+          x$CI_high,
+          ci = attributes(x$conf.int)$conf.level
+        )
       )
     } else if ("CI" %in% names(x)) {
       # param
@@ -72,11 +86,21 @@ cor_text <- function(x, show_ci = TRUE, show_statistic = TRUE, show_sig = TRUE,
   # Significance
   if (show_sig) {
     if ("p" %in% names(x)) {
-      out_text <- paste0(out_text, ", ", insight::format_p(x$p, digits = "apa", ...))
+      out_text <- paste0(
+        out_text,
+        ", ",
+        insight::format_p(x$p, digits = "apa", ...)
+      )
     } else if ("BF" %in% names(x)) {
       exact <- match.call()[["exact"]]
-      if (is.null(exact)) exact <- TRUE
-      out_text <- paste0(out_text, ", ", insight::format_bf(x$BF, exact = exact, ...))
+      if (is.null(exact)) {
+        exact <- TRUE
+      }
+      out_text <- paste0(
+        out_text,
+        ", ",
+        insight::format_bf(x$BF, exact = exact, ...)
+      )
     } else if ("pd" %in% names(x)) {
       out_text <- paste0(out_text, ", ", insight::format_pd(x$pd, ...))
     }
diff --git a/R/cor_to_ci.R b/R/cor_to_ci.R
index ce9c88e9..707ad705 100644
--- a/R/cor_to_ci.R
+++ b/R/cor_to_ci.R
@@ -5,8 +5,19 @@
 #'   better, though the Bishara and Hittner (2017) paper favours the Fieller
 #'   correction. Both are generally very similar.
 #' @export
-cor_to_ci <- function(cor, n, ci = 0.95, method = "pearson", correction = "fieller", ...) {
-  method <- match.arg(tolower(method), c("pearson", "kendall", "spearman"), several.ok = FALSE)
+cor_to_ci <- function(
+  cor,
+  n,
+  ci = 0.95,
+  method = "pearson",
+  correction = "fieller",
+  ...
+) {
+  method <- match.arg(
+    tolower(method),
+    c("pearson", "kendall", "spearman"),
+    several.ok = FALSE
+  )
 
   if (method == "kendall") {
     out <- .cor_to_ci_kendall(cor, n, ci = ci, correction = correction, ...)
@@ -43,7 +54,13 @@ cor_to_ci <- function(cor, n, ci = 0.95, method = "pearson", correction = "fiell
 
 
 # Spearman -----------------------------------------------------------------
-.cor_to_ci_spearman <- function(cor, n, ci = 0.95, correction = "fieller", ...) {
+.cor_to_ci_spearman <- function(
+  cor,
+  n,
+  ci = 0.95,
+  correction = "fieller",
+  ...
+) {
   # by @tsbaguley (https://rpubs.com/seriousstats/616206)
 
   if (correction == "fieller") {
diff --git a/R/cor_to_cov.R b/R/cor_to_cov.R
index 9ba75ff7..6d137f82 100644
--- a/R/cor_to_cov.R
+++ b/R/cor_to_cov.R
@@ -13,7 +13,12 @@
 #' cor_to_cov(cor, sd = sapply(iris[1:4], sd))
 #' cor_to_cov(cor, variance = sapply(iris[1:4], var))
 #' @export
-cor_to_cov <- function(cor, sd = NULL, variance = NULL, tol = .Machine$double.eps^(2 / 3)) {
+cor_to_cov <- function(
+  cor,
+  sd = NULL,
+  variance = NULL,
+  tol = .Machine$double.eps^(2 / 3)
+) {
   # valid matrix checks
   if (!isSquare(cor)) {
     insight::format_error("The matrix should be a square matrix.")
@@ -30,11 +35,15 @@ cor_to_cov <- function(cor, sd = NULL, variance = NULL, tol = .Machine$double.ep
   n <- nrow(cor)
 
   if (n != length(sd)) {
-    insight::format_error("The length of 'sd' or 'variance' should be the same as the number of rows of the matrix.")
+    insight::format_error(
+      "The length of 'sd' or 'variance' should be the same as the number of rows of the matrix."
+    )
   }
 
   if (length(sd[sd > 0]) != n) {
-    insight::format_error("The elements in 'sd' or 'variance' should all be non-negative.")
+    insight::format_error(
+      "The elements in 'sd' or 'variance' should all be non-negative."
+    )
   }
 
   if (isSymmetric(cor)) {
@@ -44,13 +53,18 @@ cor_to_cov <- function(cor, sd = NULL, variance = NULL, tol = .Machine$double.ep
   }
   p <- dim(cor)[1]
   quan <- p * (p - 1) / 2
-  if (isTRUE(all.equal(cor[lower.tri(cor)], rep(0, quan))) || isTRUE(all.equal(cor[upper.tri(cor)], rep(0, quan)))) {
+  if (
+    isTRUE(all.equal(cor[lower.tri(cor)], rep(0, quan))) ||
+      isTRUE(all.equal(cor[upper.tri(cor)], rep(0, quan)))
+  ) {
     is_triangular <- TRUE
   } else {
     is_triangular <- FALSE
   }
   if (!is_symmetric && !is_triangular) {
-    insight::format_error("'cor' should be either a symmetric or a triangular matrix")
+    insight::format_error(
+      "'cor' should be either a symmetric or a triangular matrix"
+    )
   }
 
   cov_matrix <- diag(sd) %*% cor %*% diag(sd)
diff --git a/R/cor_to_pcor.R b/R/cor_to_pcor.R
index ff9e7a14..17ea6b83 100644
--- a/R/cor_to_pcor.R
+++ b/R/cor_to_pcor.R
@@ -62,14 +62,16 @@ cor_to_pcor.easycormatrix <- function(cor, tol = .Machine$double.eps^(2 / 3)) {
 
 #' @export
 
-cor_to_pcor.easycorrelation <- function(cor, tol = .Machine$double.eps^(2 / 3)) {
+cor_to_pcor.easycorrelation <- function(
+  cor,
+  tol = .Machine$double.eps^(2 / 3)
+) {
   .cor_to_pcor_easycorrelation(cor = cor, tol = tol)
 }
 
 
 # pcor to cor -------------------------------------------------------------
 
-
 #' @rdname cor_to_pcor
 #' @export
 pcor_to_cor <- function(pcor, tol = .Machine$double.eps^(2 / 3)) {
@@ -95,7 +97,10 @@ pcor_to_cor.easycormatrix <- function(pcor, tol = .Machine$double.eps^(2 / 3)) {
 
 
 #' @export
-pcor_to_cor.easycorrelation <- function(pcor, tol = .Machine$double.eps^(2 / 3)) {
+pcor_to_cor.easycorrelation <- function(
+  pcor,
+  tol = .Machine$double.eps^(2 / 3)
+) {
   .cor_to_pcor_easycorrelation(pcor = pcor, tol = tol)
 }
 
@@ -103,7 +108,11 @@ pcor_to_cor.easycorrelation <- function(pcor, tol = .Machine$double.eps^(2 / 3))
 # Convenience Functions --------------------------------------------------------
 
 #' @keywords internal
-.cor_to_pcor_easycorrelation <- function(pcor = NULL, cor = NULL, tol = .Machine$double.eps^(2 / 3)) {
+.cor_to_pcor_easycorrelation <- function(
+  pcor = NULL,
+  cor = NULL,
+  tol = .Machine$double.eps^(2 / 3)
+) {
   if (is.null(cor)) {
     r <- .pcor_to_cor(.get_cor(summary(pcor, redundant = TRUE), cov = NULL))
     cor <- pcor
@@ -155,7 +164,7 @@ pcor_to_cor.easycorrelation <- function(pcor, tol = .Machine$double.eps^(2 / 3))
   # Format
   newdata <- cbind(cor[1:2], newdata)
   cor <- cor[, seq_len(ncol(newdata))]
-  cor[, ] <- newdata
+  cor[,] <- newdata
   names(cor) <- names(newdata)
 
   # P-values adjustments
@@ -167,7 +176,11 @@ pcor_to_cor.easycorrelation <- function(pcor, tol = .Machine$double.eps^(2 / 3))
 
 
 #' @keywords internal
-.cor_to_pcor_easycormatrix <- function(pcor = NULL, cor = NULL, tol = .Machine$double.eps^(2 / 3)) {
+.cor_to_pcor_easycormatrix <- function(
+  pcor = NULL,
+  cor = NULL,
+  tol = .Machine$double.eps^(2 / 3)
+) {
   if (is.null(cor)) {
     r <- .pcor_to_cor(.get_cor(pcor, cov = NULL))
     cor <- pcor
@@ -190,8 +203,16 @@ pcor_to_cor.easycorrelation <- function(pcor, tol = .Machine$double.eps^(2 / 3))
 
   # P-values adjustments
   n_comp <- sum(upper.tri(p$p))
-  p$p[upper.tri(p$p)] <- stats::p.adjust(p$p[upper.tri(p$p)], method = p_adjust, n = n_comp)
-  p$p[lower.tri(p$p)] <- stats::p.adjust(p$p[lower.tri(p$p)], method = p_adjust, n = n_comp)
+  p$p[upper.tri(p$p)] <- stats::p.adjust(
+    p$p[upper.tri(p$p)],
+    method = p_adjust,
+    n = n_comp
+  )
+  p$p[lower.tri(p$p)] <- stats::p.adjust(
+    p$p[lower.tri(p$p)],
+    method = p_adjust,
+    n = n_comp
+  )
   attributes(cor)$p_adjust <- p_adjust
 
   # Statistic and p-value
@@ -243,7 +264,9 @@ pcor_to_cor.easycorrelation <- function(pcor, tol = .Machine$double.eps^(2 / 3))
       insight::format_error("A correlation or covariance matrix is required.")
     }
     cor <- stats::cov2cor(cov)
-  } else if (inherits(cor, "easycormatrix") && colnames(cor)[1] == "Parameter") {
+  } else if (
+    inherits(cor, "easycormatrix") && colnames(cor)[1] == "Parameter"
+  ) {
     row.names(cor) <- cor$Parameter
     cor <- as.matrix(cor[-1])
   }
diff --git a/R/cor_to_spcor.R b/R/cor_to_spcor.R
index a43fb90b..b474b70b 100644
--- a/R/cor_to_spcor.R
+++ b/R/cor_to_spcor.R
@@ -1,17 +1,25 @@
 #' @rdname cor_to_pcor
 #' @export
-cor_to_spcor <- function(cor = NULL, cov = NULL, tol = .Machine$double.eps^(2 / 3)) {
+cor_to_spcor <- function(
+  cor = NULL,
+  cov = NULL,
+  tol = .Machine$double.eps^(2 / 3)
+) {
   cor <- .get_cor(cor, cov)
 
   # Semi-partial
   if (is.null(cov)) {
-    insight::format_error("Covariance matrix (or vector of SD of variables) needs to be passed for semi-partial correlations.")
+    insight::format_error(
+      "Covariance matrix (or vector of SD of variables) needs to be passed for semi-partial correlations."
+    )
   } else {
     if (!is.matrix(cov)) {
       cov <- cor_to_cov(cor, sd = cov)
     }
     inverted <- .invert_matrix(cov, tol = tol)
-    out <- -stats::cov2cor(inverted) / sqrt(diag(cov)) / sqrt(abs(diag(inverted) - t(t(inverted^2) / diag(inverted))))
+    out <- -stats::cov2cor(inverted) /
+      sqrt(diag(cov)) /
+      sqrt(abs(diag(inverted) - t(t(inverted^2) / diag(inverted))))
   }
 
   diag(out) <- 1
diff --git a/R/cormatrix_to_excel.R b/R/cormatrix_to_excel.R
index a4448ef3..75694d5c 100644
--- a/R/cormatrix_to_excel.R
+++ b/R/cormatrix_to_excel.R
@@ -41,13 +41,17 @@
 #' \dontshow{
 #' setwd(.old_wd)
 #' }
-cormatrix_to_excel <- function(data,
-                               filename,
-                               overwrite = TRUE,
-                               print.mat = TRUE,
-                               ...) {
+cormatrix_to_excel <- function(
+  data,
+  filename,
+  overwrite = TRUE,
+  print.mat = TRUE,
+  ...
+) {
   if (missing(filename)) {
-    insight::format_error("Argument 'filename' required (as per CRAN policies).")
+    insight::format_error(
+      "Argument 'filename' required (as per CRAN policies)."
+    )
   }
 
   insight::check_if_installed("openxlsx2")
@@ -409,7 +413,8 @@ cormatrix_to_excel <- function(data,
 
   # Save Excel
   cat(paste0(
-    "\n\n [Correlation matrix '", filename,
+    "\n\n [Correlation matrix '",
+    filename,
     ".xlsx' has been saved to working directory (or where specified).]"
   ))
   openxlsx2::wb_save(wb, file = paste0(filename, ".xlsx"), overwrite = TRUE)
diff --git a/R/correlation.R b/R/correlation.R
index 33288778..d9f4de87 100644
--- a/R/correlation.R
+++ b/R/correlation.R
@@ -247,29 +247,31 @@
 #'   ordinal variables. American Sociological Review. 27 (6).
 #'
 #' @export
-correlation <- function(data,
-                        data2 = NULL,
-                        select = NULL,
-                        select2 = NULL,
-                        rename = NULL,
-                        method = "pearson",
-                        missing = "keep_pairwise",
-                        p_adjust = "holm",
-                        ci = 0.95,
-                        bayesian = FALSE,
-                        bayesian_prior = "medium",
-                        bayesian_ci_method = "hdi",
-                        bayesian_test = c("pd", "rope", "bf"),
-                        redundant = FALSE,
-                        include_factors = FALSE,
-                        partial = FALSE,
-                        partial_bayesian = FALSE,
-                        multilevel = FALSE,
-                        ranktransform = FALSE,
-                        winsorize = FALSE,
-                        verbose = TRUE,
-                        standardize_names = getOption("easystats.standardize_names", FALSE),
-                        ...) {
+correlation <- function(
+  data,
+  data2 = NULL,
+  select = NULL,
+  select2 = NULL,
+  rename = NULL,
+  method = "pearson",
+  missing = "keep_pairwise",
+  p_adjust = "holm",
+  ci = 0.95,
+  bayesian = FALSE,
+  bayesian_prior = "medium",
+  bayesian_ci_method = "hdi",
+  bayesian_test = c("pd", "rope", "bf"),
+  redundant = FALSE,
+  include_factors = FALSE,
+  partial = FALSE,
+  partial_bayesian = FALSE,
+  multilevel = FALSE,
+  ranktransform = FALSE,
+  winsorize = FALSE,
+  verbose = TRUE,
+  standardize_names = getOption("easystats.standardize_names", FALSE),
+  ...
+) {
   # valid matrix checks
   if (!partial && multilevel) {
     partial <- TRUE
@@ -294,7 +296,11 @@ correlation <- function(data,
     not_in_data <- !all_selected %in% colnames(data)
     if (any(not_in_data)) {
       insight::format_error(
-        paste0("Following variables are not in the data: ", all_selected[not_in_data], collapse = ", ")
+        paste0(
+          "Following variables are not in the data: ",
+          all_selected[not_in_data],
+          collapse = ", "
+        )
       )
     }
 
@@ -308,7 +314,6 @@ correlation <- function(data,
       grp_df <- NULL
     }
 
-
     data2 <- if (!is.null(select2)) data[select2]
     data <- data[select]
 
@@ -325,7 +330,10 @@ correlation <- function(data,
     }
   }
 
-  missing <- insight::validate_argument(missing, options = c("keep_pairwise", "keep_complete"))
+  missing <- insight::validate_argument(
+    missing,
+    options = c("keep_pairwise", "keep_complete")
+  )
   if (missing == "keep_complete") {
     if (is.null(data2)) {
       oo <- stats::complete.cases(data)
@@ -405,37 +413,54 @@ correlation <- function(data,
   attr(out, "additional_arguments") <- list(...)
 
   if (inherits(data, "grouped_df")) {
-    class(out) <- unique(c("easycorrelation", "see_easycorrelation", "grouped_easycorrelation", "parameters_model", class(out)))
+    class(out) <- unique(c(
+      "easycorrelation",
+      "see_easycorrelation",
+      "grouped_easycorrelation",
+      "parameters_model",
+      class(out)
+    ))
   } else {
-    class(out) <- unique(c("easycorrelation", "see_easycorrelation", "parameters_model", class(out)))
+    class(out) <- unique(c(
+      "easycorrelation",
+      "see_easycorrelation",
+      "parameters_model",
+      class(out)
+    ))
   }
 
-  if (convert_back_to_r) out <- pcor_to_cor(pcor = out) # Revert back to r if needed.
+  if (convert_back_to_r) {
+    out <- pcor_to_cor(pcor = out)
+  } # Revert back to r if needed.
 
-  if (standardize_names) insight::standardize_names(out, ...)
+  if (standardize_names) {
+    insight::standardize_names(out, ...)
+  }
   out
 }
 
 
 #' @keywords internal
-.correlation_grouped_df <- function(data,
-                                    data2 = NULL,
-                                    method = "pearson",
-                                    p_adjust = "holm",
-                                    ci = "default",
-                                    bayesian = FALSE,
-                                    bayesian_prior = "medium",
-                                    bayesian_ci_method = "hdi",
-                                    bayesian_test = c("pd", "rope", "bf"),
-                                    redundant = FALSE,
-                                    include_factors = TRUE,
-                                    partial = FALSE,
-                                    partial_bayesian = FALSE,
-                                    multilevel = FALSE,
-                                    ranktransform = FALSE,
-                                    winsorize = FALSE,
-                                    verbose = TRUE,
-                                    ...) {
+.correlation_grouped_df <- function(
+  data,
+  data2 = NULL,
+  method = "pearson",
+  p_adjust = "holm",
+  ci = "default",
+  bayesian = FALSE,
+  bayesian_prior = "medium",
+  bayesian_ci_method = "hdi",
+  bayesian_test = c("pd", "rope", "bf"),
+  redundant = FALSE,
+  include_factors = TRUE,
+  partial = FALSE,
+  partial_bayesian = FALSE,
+  multilevel = FALSE,
+  ranktransform = FALSE,
+  winsorize = FALSE,
+  verbose = TRUE,
+  ...
+) {
   groups <- setdiff(colnames(attributes(data)$groups), ".rows")
   ungrouped_x <- as.data.frame(data)
   xlist <- split(ungrouped_x, ungrouped_x[groups], sep = " - ")
@@ -473,7 +498,9 @@ correlation <- function(data,
     if (inherits(data2, "grouped_df")) {
       groups2 <- setdiff(colnames(attributes(data2)$groups), ".rows")
       if (!all.equal(groups, groups2)) {
-        insight::format_error("'data2' should have the same grouping characteristics as data.")
+        insight::format_error(
+          "'data2' should have the same grouping characteristics as data."
+        )
       }
       ungrouped_y <- as.data.frame(data2)
       ylist <- split(ungrouped_y, ungrouped_y[groups], sep = " - ")
@@ -515,31 +542,35 @@ correlation <- function(data,
 
 
 #' @keywords internal
-.correlation <- function(data,
-                         data2 = NULL,
-                         method = "pearson",
-                         p_adjust = "holm",
-                         ci = "default",
-                         bayesian = FALSE,
-                         bayesian_prior = "medium",
-                         bayesian_ci_method = "hdi",
-                         bayesian_test = c("pd", "rope", "bf"),
-                         redundant = FALSE,
-                         include_factors = FALSE,
-                         partial = FALSE,
-                         partial_bayesian = FALSE,
-                         multilevel = FALSE,
-                         ranktransform = FALSE,
-                         winsorize = FALSE,
-                         verbose = TRUE,
-                         ...) {
+.correlation <- function(
+  data,
+  data2 = NULL,
+  method = "pearson",
+  p_adjust = "holm",
+  ci = "default",
+  bayesian = FALSE,
+  bayesian_prior = "medium",
+  bayesian_ci_method = "hdi",
+  bayesian_test = c("pd", "rope", "bf"),
+  redundant = FALSE,
+  include_factors = FALSE,
+  partial = FALSE,
+  partial_bayesian = FALSE,
+  multilevel = FALSE,
+  ranktransform = FALSE,
+  winsorize = FALSE,
+  verbose = TRUE,
+  ...
+) {
   if (!is.null(data2)) {
     data <- cbind(data, data2)
   }
 
   if (ncol(data) <= 2L && any(sapply(data, is.factor)) && !include_factors) {
     if (isTRUE(verbose)) {
-      insight::format_warning("It seems like there is not enough continuous variables in your data. Maybe you want to include the factors? We're setting `include_factors=TRUE` for you.")
+      insight::format_warning(
+        "It seems like there is not enough continuous variables in your data. Maybe you want to include the factors? We're setting `include_factors=TRUE` for you."
+      )
     }
     include_factors <- TRUE
   }
@@ -547,7 +578,10 @@ correlation <- function(data,
   # valid matrix checks ----------------
 
   # What if only factors
-  if (sum(sapply(if (is.null(data2)) data else cbind(data, data2), is.numeric)) == 0) {
+  if (
+    sum(sapply(if (is.null(data2)) data else cbind(data, data2), is.numeric)) ==
+      0
+  ) {
     include_factors <- TRUE
   }
 
@@ -571,7 +605,11 @@ correlation <- function(data,
     multilevel = multilevel,
     method = method
   )
-  data <- .clean_data(data, include_factors = include_factors, multilevel = multilevel)
+  data <- .clean_data(
+    data,
+    include_factors = include_factors,
+    multilevel = multilevel
+  )
 
   # LOOP ----------------
 
@@ -613,7 +651,9 @@ correlation <- function(data,
         if ("r" %in% names(result) && !"r" %in% names(params)) {
           names(params)[names(params) %in% c("rho", "tau")] <- "r"
         }
-        if (!"r" %in% names(params) && any(c("rho", "tau") %in% names(result))) {
+        if (
+          !"r" %in% names(params) && any(c("rho", "tau") %in% names(result))
+        ) {
           names(params)[names(params) %in% c("rho", "tau")] <- "r"
           names(result)[names(result) %in% c("rho", "tau")] <- "r"
         }
diff --git a/R/display.R b/R/display.R
index 986fc3ea..bc9eec04 100644
--- a/R/display.R
+++ b/R/display.R
@@ -30,13 +30,15 @@
 #' s <- summary(corr)
 #' display(s)
 #' @export
-display.easycormatrix <- function(object,
-                                  format = "markdown",
-                                  digits = 2,
-                                  p_digits = 3,
-                                  stars = TRUE,
-                                  include_significance = NULL,
-                                  ...) {
+display.easycormatrix <- function(
+  object,
+  format = "markdown",
+  digits = 2,
+  p_digits = 3,
+  stars = TRUE,
+  include_significance = NULL,
+  ...
+) {
   if (format == "markdown") {
     print_md(
       x = object,
@@ -60,12 +62,14 @@ display.easycormatrix <- function(object,
 
 
 #' @export
-display.easycorrelation <- function(object,
-                                    format = "markdown",
-                                    digits = 2,
-                                    p_digits = 3,
-                                    stars = TRUE,
-                                    ...) {
+display.easycorrelation <- function(
+  object,
+  format = "markdown",
+  digits = 2,
+  p_digits = 3,
+  stars = TRUE,
+  ...
+) {
   if (format == "markdown") {
     print_md(
       x = object,
diff --git a/R/methods.easycorrelation.R b/R/methods.easycorrelation.R
index 22c2f17c..1198fd70 100644
--- a/R/methods.easycorrelation.R
+++ b/R/methods.easycorrelation.R
@@ -2,10 +2,12 @@
 # @examples
 # summary(correlation(mtcars), target = "p")
 #' @export
-summary.easycorrelation <- function(object,
-                                    redundant = FALSE,
-                                    target = NULL,
-                                    ...) {
+summary.easycorrelation <- function(
+  object,
+  redundant = FALSE,
+  target = NULL,
+  ...
+) {
   # If data2 is present
   if (!is.null(attributes(object)$data2)) {
     redundant <- FALSE
@@ -19,36 +21,63 @@ summary.easycorrelation <- function(object,
   }
 
   if (is.null(target)) {
-    target <- names(object)[names(object) %in% c("r", "rho", "tau", "Median", "Dxy")][1]
+    target <- names(object)[
+      names(object) %in% c("r", "rho", "tau", "Median", "Dxy")
+    ][1]
     if (is.na(target)) {
-      target <- names(object)[!names(object) %in% c("Parameter1", "Parameter2")][1]
+      target <- names(object)[
+        !names(object) %in% c("Parameter1", "Parameter2")
+      ][1]
     }
   } else {
     target <- target[target %in% names(object)][1]
     if (is.na(target) || length(target) == 0) {
-      stop("`target` must be a column name in the correlation object.", call. = FALSE)
+      stop(
+        "`target` must be a column name in the correlation object.",
+        call. = FALSE
+      )
     }
   }
 
-  out <- .create_matrix(cormatrix, object, column = target, redundant = redundant)
+  out <- .create_matrix(
+    cormatrix,
+    object,
+    column = target,
+    redundant = redundant
+  )
 
   # Fill attributes
-  for (i in names(object)[!names(object) %in% c("Group", "Parameter1", "Parameter2", target)]) {
-    attri <- .create_matrix(cormatrix, object, column = i, redundant = redundant)
+  for (i in names(object)[
+    !names(object) %in% c("Group", "Parameter1", "Parameter2", target)
+  ]) {
+    attri <- .create_matrix(
+      cormatrix,
+      object,
+      column = i,
+      redundant = redundant
+    )
     attr(out, i) <- attri
   }
 
   # Transfer attributes
   attributes(out) <- c(
     attributes(out),
-    attributes(object)[!names(attributes(object)) %in% c("names", "row.names", "class", names(attributes(out)))]
+    attributes(object)[
+      !names(attributes(object)) %in%
+        c("names", "row.names", "class", names(attributes(out)))
+    ]
   )
   attributes(out) <- c(attributes(out), list(...))
   attr(out, "redundant") <- redundant
   attr(out, "coefficient_name") <- target
 
   if (inherits(object, "grouped_easycorrelation")) {
-    class(out) <- c("easycormatrix", "see_easycormatrix", "grouped_easycormatrix", class(out))
+    class(out) <- c(
+      "easycormatrix",
+      "see_easycormatrix",
+      "grouped_easycormatrix",
+      class(out)
+    )
   } else {
     class(out) <- c("easycormatrix", "see_easycormatrix", class(out))
   }
@@ -120,7 +149,15 @@ as.list.easycorrelation <- function(x, cols = NULL, redundant = FALSE, ...) {
 #' @export
 standardize_names.easycorrelation <- function(data, ...) {
   ori <- data
-  names(data)[names(data) == datawizard::extract_column_names(data, select = "(rho|tau)", regex = TRUE, verbose = FALSE)] <- "r"
+  names(data)[
+    names(data) ==
+      datawizard::extract_column_names(
+        data,
+        select = "(rho|tau)",
+        regex = TRUE,
+        verbose = FALSE
+      )
+  ] <- "r"
   data <- insight::standardize_names(as.data.frame(data), ...)
   class(data) <- class(ori)
   data
@@ -152,7 +189,11 @@ standardize_names.easycorrelation <- function(data, ...) {
 .fill_matrix <- function(frame, object, column = "r", redundant = TRUE) {
   for (row in row.names(frame)) {
     for (col in colnames(frame)) {
-      frame[row, col] <- object[(object$Parameter1 == row & object$Parameter2 == col) | (object$Parameter2 == row & object$Parameter1 == col), column][1]
+      frame[row, col] <- object[
+        (object$Parameter1 == row & object$Parameter2 == col) |
+          (object$Parameter2 == row & object$Parameter1 == col),
+        column
+      ][1]
     }
   }
 
@@ -179,7 +220,9 @@ standardize_names.easycorrelation <- function(data, ...) {
   }
 
   if (is.null(cols)) {
-    cols <- colnames(object)[!colnames(object) %in% c("Group", "Parameter1", "Parameter2")]
+    cols <- colnames(object)[
+      !colnames(object) %in% c("Group", "Parameter1", "Parameter2")
+    ]
   }
 
   sx <- summary(object = object, redundant = redundant, ...)
@@ -204,7 +247,10 @@ standardize_names.easycorrelation <- function(data, ...) {
   attributes(lx) <- c(
     attributes(lx),
     class = "easymatrixlist",
-    attributes(sx)[!names(attributes(sx)) %in% c("names", "row.names", "class", "coefficient_name", names(lx))]
+    attributes(sx)[
+      !names(attributes(sx)) %in%
+        c("names", "row.names", "class", "coefficient_name", names(lx))
+    ]
   )
 
   lx
diff --git a/R/methods_format.R b/R/methods_format.R
index acf80d20..a839bcc7 100644
--- a/R/methods_format.R
+++ b/R/methods_format.R
@@ -1,12 +1,14 @@
 # Correlation table ---------------------------------------------------------
 
 #' @export
-format.easycorrelation <- function(x,
-                                   digits = NULL,
-                                   p_digits = NULL,
-                                   stars = NULL,
-                                   format = NULL,
-                                   ...) {
+format.easycorrelation <- function(
+  x,
+  digits = NULL,
+  p_digits = NULL,
+  stars = NULL,
+  format = NULL,
+  ...
+) {
   if (nrow(x) == 0) {
     warning("The table is empty, no rows left to print.", call. = FALSE)
     return(as.data.frame(x))
@@ -14,7 +16,8 @@ format.easycorrelation <- function(x,
 
   attri <- attributes(x)
 
-  out <- insight::format_table(x,
+  out <- insight::format_table(
+    x,
     digits = .retrieve_arg_from_attr(attri, digits, default = 2),
     stars = .retrieve_arg_from_attr(attri, stars, default = TRUE),
     p_digits = .retrieve_arg_from_attr(attri, p_digits, default = "apa"),
@@ -24,25 +27,32 @@ format.easycorrelation <- function(x,
   out$Method <- NULL
   out$n_Obs <- NULL
 
-  attr(out, "table_footer") <- .format_easycorrelation_footer(x, format = format)
-  attr(out, "table_caption") <- .format_easycorrelation_caption(x, format = format)
+  attr(out, "table_footer") <- .format_easycorrelation_footer(
+    x,
+    format = format
+  )
+  attr(out, "table_caption") <- .format_easycorrelation_caption(
+    x,
+    format = format
+  )
   out
 }
 
 
 # Correlation matrix -----------------------------------------------------------
 
-
 #' @export
-format.easycormatrix <- function(x,
-                                 digits = NULL,
-                                 p_digits = NULL,
-                                 stars = NULL,
-                                 include_significance = NULL,
-                                 format = NULL,
-                                 zap_small = NULL,
-                                 bf_exact = TRUE,
-                                 ...) {
+format.easycormatrix <- function(
+  x,
+  digits = NULL,
+  p_digits = NULL,
+  stars = NULL,
+  include_significance = NULL,
+  format = NULL,
+  zap_small = NULL,
+  bf_exact = TRUE,
+  ...
+) {
   # If it's a real matrix
   if (!"Parameter" %in% colnames(x)) {
     m <- as.data.frame(x)
@@ -55,27 +65,30 @@ format.easycormatrix <- function(x,
   # Retrieve arguments from attributes (or assign default)
   digits <- .retrieve_arg_from_attr(attri, digits, default = 2)
   stars <- .retrieve_arg_from_attr(attri, stars, default = TRUE)
-  include_significance <- .retrieve_arg_from_attr(attri, include_significance, default = FALSE)
+  include_significance <- .retrieve_arg_from_attr(
+    attri,
+    include_significance,
+    default = FALSE
+  )
   p_digits <- .retrieve_arg_from_attr(attri, p_digits, default = "apa")
   zap_small <- .retrieve_arg_from_attr(attri, zap_small, default = TRUE)
 
-
   # Round and format values
   nums <- sapply(as.data.frame(x), is.numeric)
   x[, nums] <- sapply(
     as.data.frame(x)[, nums],
     insight::format_value,
-    digits = digits, zap_small = zap_small, ...
+    digits = digits,
+    zap_small = zap_small,
+    ...
   )
 
-
   # Deduct if stars only
   stars_only <- FALSE
   if (!include_significance && stars) {
     stars_only <- TRUE
   }
 
-
   # Significance
   type <- names(attri)[names(attri) %in% c("BF", "pd", "p")][1]
   sig <- attri[[type]]
@@ -111,11 +124,16 @@ format.easycormatrix <- function(x,
     }
 
     if (!stars_only) {
-      sig[, nums] <- sapply(sig[, nums], function(x) ifelse(x != "", paste0(" (", x, ")"), "")) # nolint
+      sig[, nums] <- sapply(sig[, nums], function(x) {
+        ifelse(x != "", paste0(" (", x, ")"), "")
+      }) # nolint
     }
 
     if (include_significance || stars) {
-      x[, nums] <- paste0(as.matrix(as.data.frame(x)[, nums]), as.matrix(sig[, nums]))
+      x[, nums] <- paste0(
+        as.matrix(as.data.frame(x)[, nums]),
+        as.matrix(sig[, nums])
+      )
     }
   }
 
@@ -129,8 +147,14 @@ format.easycormatrix <- function(x,
     }
   }
 
-  attr(out, "table_footer") <- .format_easycorrelation_footer(x, format = format)
-  attr(out, "table_caption") <- .format_easycorrelation_caption(x, format = format)
+  attr(out, "table_footer") <- .format_easycorrelation_footer(
+    x,
+    format = format
+  )
+  attr(out, "table_caption") <- .format_easycorrelation_caption(
+    x,
+    format = format
+  )
   out
 }
 
@@ -169,7 +193,8 @@ format.easycormatrix <- function(x,
     # remove empty elements
     footer <- footer[nzchar(footer, keepNA = TRUE)]
     # create list or separate by ";"
-    footer <- switch(format,
+    footer <- switch(
+      format,
       html = paste(footer, collapse = "; "),
       as.list(footer)
     )
@@ -190,7 +215,10 @@ format.easycormatrix <- function(x,
       prefix <- "Correlation Matrix ("
     }
     if (is.null(format) || format == "text") {
-      caption <- c(paste0("# ", prefix, unique(attributes(x)$method), "-method)"), "blue")
+      caption <- c(
+        paste0("# ", prefix, unique(attributes(x)$method), "-method)"),
+        "blue"
+      )
     } else {
       caption <- paste0(prefix, unique(attributes(x)$method), "-method)")
     }
@@ -201,7 +229,6 @@ format.easycormatrix <- function(x,
 
 # Arguments retrieving ----------------------------------------------------
 
-
 #' @keywords internal
 .retrieve_arg_from_attr <- function(attri, arg, default) {
   arg_name <- deparse(substitute(arg))
@@ -209,7 +236,11 @@ format.easycormatrix <- function(x,
   if (is.null(arg)) {
     if (arg_name %in% names(attri)) {
       arg <- attri[[arg_name]]
-    } else if ("additional_arguments" %in% names(attri) && arg_name %in% names(attri$additional_arguments)) {
+    } else if (
+      "additional_arguments" %in%
+        names(attri) &&
+        arg_name %in% names(attri$additional_arguments)
+    ) {
       arg <- attri$additional_arguments[[arg_name]]
     } else {
       arg <- default # That's the real default
diff --git a/R/methods_print.R b/R/methods_print.R
index 1a9bd54d..91e6a67d 100644
--- a/R/methods_print.R
+++ b/R/methods_print.R
@@ -1,6 +1,5 @@
 # Console -----------------------------------------------------------------
 
-
 #' @export
 print.easycorrelation <- function(x, ...) {
   cat(insight::export_table(format(x, ...), ...))
@@ -39,7 +38,16 @@ print.easymatrixlist <- function(x, cols = "auto", ...) {
 #' @export
 print.grouped_easymatrixlist <- function(x, cols = "auto", ...) {
   for (i in names(x)) {
-    cat(rep("=", nchar(i) + 2), "\n ", i, " ", "\n", rep("=", nchar(i) + 2), "\n\n", sep = "")
+    cat(
+      rep("=", nchar(i) + 2),
+      "\n ",
+      i,
+      " ",
+      "\n",
+      rep("=", nchar(i) + 2),
+      "\n\n",
+      sep = ""
+    )
     print(x[[i]], ...)
     cat("\n")
   }
@@ -47,12 +55,14 @@ print.grouped_easymatrixlist <- function(x, cols = "auto", ...) {
 
 # MD and HTML --------------------------------------------------------------
 
-.print_md_html_easycorrelation <- function(x,
-                                           digits = NULL,
-                                           p_digits = NULL,
-                                           stars = NULL,
-                                           format = "markdown",
-                                           ...) {
+.print_md_html_easycorrelation <- function(
+  x,
+  digits = NULL,
+  p_digits = NULL,
+  stars = NULL,
+  format = "markdown",
+  ...
+) {
   formatted_table <- format(
     x,
     digits = digits,
@@ -75,11 +85,13 @@ print.grouped_easymatrixlist <- function(x, cols = "auto", ...) {
 
 #' @rdname display.easycormatrix
 #' @export
-print_md.easycorrelation <- function(x,
-                                     digits = NULL,
-                                     p_digits = NULL,
-                                     stars = NULL,
-                                     ...) {
+print_md.easycorrelation <- function(
+  x,
+  digits = NULL,
+  p_digits = NULL,
+  stars = NULL,
+  ...
+) {
   .print_md_html_easycorrelation(
     x,
     digits = digits,
@@ -93,11 +105,13 @@ print_md.easycorrelation <- function(x,
 
 #' @rdname display.easycormatrix
 #' @export
-print_html.easycorrelation <- function(x,
-                                       digits = NULL,
-                                       p_digits = NULL,
-                                       stars = NULL,
-                                       ...) {
+print_html.easycorrelation <- function(
+  x,
+  digits = NULL,
+  p_digits = NULL,
+  stars = NULL,
+  ...
+) {
   .print_md_html_easycorrelation(
     x,
     digits = digits,
@@ -109,13 +123,15 @@ print_html.easycorrelation <- function(x,
 }
 
 
-.print_md_html_easycormatrix <- function(x,
-                                         digits = NULL,
-                                         p_digits = NULL,
-                                         stars = NULL,
-                                         include_significance = NULL,
-                                         format = "markdown",
-                                         ...) {
+.print_md_html_easycormatrix <- function(
+  x,
+  digits = NULL,
+  p_digits = NULL,
+  stars = NULL,
+  include_significance = NULL,
+  format = "markdown",
+  ...
+) {
   formatted_table <- format(
     x,
     digits = digits,
@@ -139,12 +155,14 @@ print_html.easycorrelation <- function(x,
 
 #' @rdname display.easycormatrix
 #' @export
-print_md.easycormatrix <- function(x,
-                                   digits = NULL,
-                                   p_digits = NULL,
-                                   stars = NULL,
-                                   include_significance = NULL,
-                                   ...) {
+print_md.easycormatrix <- function(
+  x,
+  digits = NULL,
+  p_digits = NULL,
+  stars = NULL,
+  include_significance = NULL,
+  ...
+) {
   .print_md_html_easycormatrix(
     x,
     digits = digits,
@@ -159,12 +177,14 @@ print_md.easycormatrix <- function(x,
 
 #' @rdname display.easycormatrix
 #' @export
-print_html.easycormatrix <- function(x,
-                                     digits = NULL,
-                                     p_digits = NULL,
-                                     stars = NULL,
-                                     include_significance = NULL,
-                                     ...) {
+print_html.easycormatrix <- function(
+  x,
+  digits = NULL,
+  p_digits = NULL,
+  stars = NULL,
+  include_significance = NULL,
+  ...
+) {
   .print_md_html_easycormatrix(
     x,
     digits = digits,
diff --git a/R/utils_create_diagonal.R b/R/utils_create_diagonal.R
index 11d6350a..83ef48bf 100644
--- a/R/utils_create_diagonal.R
+++ b/R/utils_create_diagonal.R
@@ -5,39 +5,95 @@
     "Parameter2" = unique(params$Parameter1)
   )
 
-  if ("Group" %in% names(params)) diagonal$Group <- unique(params$Group)[1]
-  if ("r" %in% names(params)) diagonal$r <- 1
-  if ("rho" %in% names(params)) diagonal$rho <- 1
-  if ("tau" %in% names(params)) diagonal$tau <- 1
-  if ("p" %in% names(params)) diagonal$p <- 0
-  if ("t" %in% names(params)) diagonal$t <- Inf
-  if ("S" %in% names(params)) diagonal$S <- Inf
-  if ("z" %in% names(params)) diagonal$z <- Inf
-  if ("df" %in% names(params)) diagonal$df <- unique(params$df)[1]
-  if ("df_error" %in% names(params)) diagonal$df_error <- unique(params$df_error)[1]
-  if ("CI" %in% names(params)) diagonal$CI <- unique(params$CI)[1]
-  if ("CI_low" %in% names(params)) diagonal$CI_low <- 1
-  if ("CI_high" %in% names(params)) diagonal$CI_high <- 1
-  if ("Method" %in% names(params)) diagonal$Method <- unique(params$Method)[1]
-  if ("n_Obs" %in% names(params)) diagonal$n_Obs <- unique(params$n_Obs)[1]
+  if ("Group" %in% names(params)) {
+    diagonal$Group <- unique(params$Group)[1]
+  }
+  if ("r" %in% names(params)) {
+    diagonal$r <- 1
+  }
+  if ("rho" %in% names(params)) {
+    diagonal$rho <- 1
+  }
+  if ("tau" %in% names(params)) {
+    diagonal$tau <- 1
+  }
+  if ("p" %in% names(params)) {
+    diagonal$p <- 0
+  }
+  if ("t" %in% names(params)) {
+    diagonal$t <- Inf
+  }
+  if ("S" %in% names(params)) {
+    diagonal$S <- Inf
+  }
+  if ("z" %in% names(params)) {
+    diagonal$z <- Inf
+  }
+  if ("df" %in% names(params)) {
+    diagonal$df <- unique(params$df)[1]
+  }
+  if ("df_error" %in% names(params)) {
+    diagonal$df_error <- unique(params$df_error)[1]
+  }
+  if ("CI" %in% names(params)) {
+    diagonal$CI <- unique(params$CI)[1]
+  }
+  if ("CI_low" %in% names(params)) {
+    diagonal$CI_low <- 1
+  }
+  if ("CI_high" %in% names(params)) {
+    diagonal$CI_high <- 1
+  }
+  if ("Method" %in% names(params)) {
+    diagonal$Method <- unique(params$Method)[1]
+  }
+  if ("n_Obs" %in% names(params)) {
+    diagonal$n_Obs <- unique(params$n_Obs)[1]
+  }
 
   # Bayesian
-  if ("Median" %in% names(params)) diagonal$Median <- 1
-  if ("Mean" %in% names(params)) diagonal$Mean <- 1
-  if ("MAP" %in% names(params)) diagonal$MAP <- 1
-  if ("SD" %in% names(params)) diagonal$SD <- 0
-  if ("MAD" %in% names(params)) diagonal$MAD <- 0
-  if ("pd" %in% names(params)) diagonal$pd <- 1
-  if ("ROPE_Percentage" %in% names(params)) diagonal$ROPE_Percentage <- 0
-  if ("BF" %in% names(params)) diagonal$BF <- Inf
-  if ("log_BF" %in% names(params)) diagonal$log_BF <- Inf
-  if ("Prior_Distribution" %in% names(params)) diagonal$Prior_Distribution <- unique(params$Prior_Distribution)[1]
-  if ("Prior_Location" %in% names(params)) diagonal$Prior_Location <- unique(params$Prior_Location)[1]
-  if ("Prior_Scale" %in% names(params)) diagonal$Prior_Scale <- unique(params$Prior_Scale)[1]
+  if ("Median" %in% names(params)) {
+    diagonal$Median <- 1
+  }
+  if ("Mean" %in% names(params)) {
+    diagonal$Mean <- 1
+  }
+  if ("MAP" %in% names(params)) {
+    diagonal$MAP <- 1
+  }
+  if ("SD" %in% names(params)) {
+    diagonal$SD <- 0
+  }
+  if ("MAD" %in% names(params)) {
+    diagonal$MAD <- 0
+  }
+  if ("pd" %in% names(params)) {
+    diagonal$pd <- 1
+  }
+  if ("ROPE_Percentage" %in% names(params)) {
+    diagonal$ROPE_Percentage <- 0
+  }
+  if ("BF" %in% names(params)) {
+    diagonal$BF <- Inf
+  }
+  if ("log_BF" %in% names(params)) {
+    diagonal$log_BF <- Inf
+  }
+  if ("Prior_Distribution" %in% names(params)) {
+    diagonal$Prior_Distribution <- unique(params$Prior_Distribution)[1]
+  }
+  if ("Prior_Location" %in% names(params)) {
+    diagonal$Prior_Location <- unique(params$Prior_Location)[1]
+  }
+  if ("Prior_Scale" %in% names(params)) {
+    diagonal$Prior_Scale <- unique(params$Prior_Scale)[1]
+  }
 
   for (var in names(params)[!names(params) %in% names(diagonal)]) {
     if (length(unique(params[[var]])) > 1L) {
-      insight::format_error("Something's unexpected happened when creating the diagonal data. Please open an issue at https://github.com/easystats/correlation/issues")
+      insight::format_error(
+        "Something's unexpected happened when creating the diagonal data. Please open an issue at https://github.com/easystats/correlation/issues"
+      )
     }
     diagonal[[var]] <- unique(params[[var]])[1]
   }
diff --git a/R/utils_get_combinations.R b/R/utils_get_combinations.R
index 4c9612be..60849f6a 100644
--- a/R/utils_get_combinations.R
+++ b/R/utils_get_combinations.R
@@ -1,11 +1,17 @@
 #' @keywords internal
-.get_combinations <- function(data,
-                              data2 = NULL,
-                              redundant = TRUE,
-                              include_factors = TRUE,
-                              multilevel = FALSE,
-                              method = "pearson") {
-  data <- .clean_data(data, include_factors = include_factors, multilevel = multilevel)
+.get_combinations <- function(
+  data,
+  data2 = NULL,
+  redundant = TRUE,
+  include_factors = TRUE,
+  multilevel = FALSE,
+  method = "pearson"
+) {
+  data <- .clean_data(
+    data,
+    include_factors = include_factors,
+    multilevel = multilevel
+  )
 
   if (method == "polychoric") {
     vars <- names(data)
@@ -15,18 +21,23 @@
     vars <- names(data)
   }
 
-
   # Find pairs
   if (is.null(data2)) {
     vars2 <- vars
   } else {
-    data2 <- .clean_data(data2, include_factors = include_factors, multilevel = multilevel)
+    data2 <- .clean_data(
+      data2,
+      include_factors = include_factors,
+      multilevel = multilevel
+    )
     data2_nums <- data2[sapply(data2, is.numeric)]
     vars2 <- names(data2_nums)
   }
 
   combinations <- expand.grid(vars, vars2, stringsAsFactors = FALSE)
-  combinations <- combinations[order(match(combinations$Var1, vars), match(combinations$Var2, vars2)), ]
+  combinations <- combinations[
+    order(match(combinations$Var1, vars), match(combinations$Var2, vars2)),
+  ]
 
   row.names(combinations) <- NULL
   names(combinations) <- c("Parameter1", "Parameter2")
diff --git a/R/utils_get_matrix.R b/R/utils_get_matrix.R
index fcc6d610..472a4fc1 100644
--- a/R/utils_get_matrix.R
+++ b/R/utils_get_matrix.R
@@ -1,6 +1,10 @@
 #' @keywords internal
 .get_matrix <- function(data, square = FALSE) {
-  if ((all(data$Parameter1 %in% data$Parameter2) && all(data$Parameter2 %in% data$Parameter1)) || square) {
+  if (
+    (all(data$Parameter1 %in% data$Parameter2) &&
+      all(data$Parameter2 %in% data$Parameter1)) ||
+      square
+  ) {
     vars <- as.character(unique(c(data$Parameter1, data$Parameter2)))
     dim <- length(vars)
     m <- matrix(nrow = dim, ncol = dim, dimnames = list(vars, vars))
diff --git a/R/utils_remove_redundant.R b/R/utils_remove_redundant.R
index abb07266..89008ba7 100644
--- a/R/utils_remove_redundant.R
+++ b/R/utils_remove_redundant.R
@@ -1,10 +1,15 @@
 #' @keywords internal
 .remove_redundant <- function(params) {
-  if (all(params$Parameter1 %in% params$Parameter2) && all(params$Parameter2 %in% params$Parameter1)) {
+  if (
+    all(params$Parameter1 %in% params$Parameter2) &&
+      all(params$Parameter2 %in% params$Parameter1)
+  ) {
     m <- .get_matrix(params)
     m[upper.tri(m, diag = TRUE)] <- NA
     rows_NA <- .get_rows_non_NA(m)
-    out <- params[!paste0(params$Parameter1, "_", params$Parameter2) %in% rows_NA, ]
+    out <- params[
+      !paste0(params$Parameter1, "_", params$Parameter2) %in% rows_NA,
+    ]
   } else {
     # Might be some edgecases here
     out <- params
@@ -23,7 +28,10 @@
 
   # inverse parameters
   inversed <- params
-  inversed[, c("Parameter1", "Parameter2")] <- params[, c("Parameter2", "Parameter1")]
+  inversed[, c("Parameter1", "Parameter2")] <- params[, c(
+    "Parameter2",
+    "Parameter1"
+  )]
 
   # bind and get diagonal data
   params <- rbind(params, inversed)
@@ -38,7 +46,12 @@
 
   # Reorder
   if (!is.null(data)) {
-    params <- params[order(match(params$Parameter1, names(data)), match(params$Parameter2, names(data))), ]
+    params <- params[
+      order(
+        match(params$Parameter1, names(data)),
+        match(params$Parameter2, names(data))
+      ),
+    ]
   }
 
   params
diff --git a/R/visualisation_recipe.cor_test.R b/R/visualisation_recipe.cor_test.R
index 7dc69c4d..0396c14b 100644
--- a/R/visualisation_recipe.cor_test.R
+++ b/R/visualisation_recipe.cor_test.R
@@ -16,25 +16,31 @@
 #' )
 #' }
 #' @export
-visualisation_recipe.easycor_test <- function(x,
-                                              show_data = "point",
-                                              show_text = "subtitle",
-                                              smooth = NULL,
-                                              point = NULL,
-                                              text = NULL,
-                                              labs = NULL,
-                                              ...) {
+visualisation_recipe.easycor_test <- function(
+  x,
+  show_data = "point",
+  show_text = "subtitle",
+  smooth = NULL,
+  point = NULL,
+  text = NULL,
+  labs = NULL,
+  ...
+) {
   data <- attributes(x)$data
 
   # Text
   subtitle <- NULL
   title <- NULL
-  if (!is.null(show_text) && show_text == "subtitle") subtitle <- cor_text(x, ...)
-  if (!is.null(show_text) && show_text == "title") title <- cor_text(x, ...)
-
+  if (!is.null(show_text) && show_text == "subtitle") {
+    subtitle <- cor_text(x, ...)
+  }
+  if (!is.null(show_text) && show_text == "title") {
+    title <- cor_text(x, ...)
+  }
 
   # Get scatter plot
-  layers <- .see_scatter(data,
+  layers <- .see_scatter(
+    data,
     cor_results = x,
     x = x$Parameter1,
     y = x$Parameter2,
@@ -50,11 +56,16 @@ visualisation_recipe.easycor_test <- function(x,
   )
 
   # Text
-  if (!is.null(show_text) && isTRUE(show_text) && show_text %in% c("text", "label")) {
+  if (
+    !is.null(show_text) &&
+      isTRUE(show_text) &&
+      show_text %in% c("text", "label")
+  ) {
     # Add text
     x$label <- cor_text(x, ...)
     x$label_x <- max(data[[x$Parameter1]], na.rm = TRUE)
-    x$label_y <- max(data[[x$Parameter2]], na.rm = TRUE) + 0.05 * diff(range(data[[x$Parameter2]], na.rm = TRUE))
+    x$label_y <- max(data[[x$Parameter2]], na.rm = TRUE) +
+      0.05 * diff(range(data[[x$Parameter2]], na.rm = TRUE))
 
     l <- paste0("l", length(layers) + 1)
     layers[[l]] <- list(
@@ -71,7 +82,11 @@ visualisation_recipe.easycor_test <- function(x,
   }
 
   # Out
-  class(layers) <- c("visualisation_recipe", "see_visualisation_recipe", class(layers))
+  class(layers) <- c(
+    "visualisation_recipe",
+    "see_visualisation_recipe",
+    class(layers)
+  )
   attr(layers, "data") <- data
   layers
 }
@@ -79,21 +94,22 @@ visualisation_recipe.easycor_test <- function(x,
 
 # see_scatter -------------------------------------------------------------
 
-
-.see_scatter <- function(data,
-                         cor_results,
-                         x,
-                         y,
-                         show_data = "point",
-                         show_text = "text",
-                         smooth = NULL,
-                         point = NULL,
-                         text = NULL,
-                         labs = NULL,
-                         title = NULL,
-                         subtitle = NULL,
-                         type = show_data,
-                         ...) {
+.see_scatter <- function(
+  data,
+  cor_results,
+  x,
+  y,
+  show_data = "point",
+  show_text = "text",
+  smooth = NULL,
+  point = NULL,
+  text = NULL,
+  labs = NULL,
+  title = NULL,
+  subtitle = NULL,
+  type = show_data,
+  ...
+) {
   # Keep only relevant variables (lighter) and complete cases
   data <- data[stats::complete.cases(data[c(x, y)]), ]
 
@@ -119,7 +135,10 @@ visualisation_recipe.easycor_test <- function(x,
     )
   )
   if (!is.null(smooth)) {
-    layers[[paste0("l", l)]] <- utils::modifyList(layers[[paste0("l", l)]], smooth)
+    layers[[paste0("l", l)]] <- utils::modifyList(
+      layers[[paste0("l", l)]],
+      smooth
+    )
   }
   l <- l + 1
 
@@ -133,7 +152,10 @@ visualisation_recipe.easycor_test <- function(x,
     )
   )
   if (!is.null(point)) {
-    layers[[paste0("l", l)]] <- utils::modifyList(layers[[paste0("l", l)]], point)
+    layers[[paste0("l", l)]] <- utils::modifyList(
+      layers[[paste0("l", l)]],
+      point
+    )
   }
   l <- l + 1
 
@@ -157,9 +179,16 @@ visualisation_recipe.easycor_test <- function(x,
   # l <- l + 1
 
   # Labs
-  layers[[paste0("l", l)]] <- list(geom = "labs", subtitle = subtitle, title = title)
+  layers[[paste0("l", l)]] <- list(
+    geom = "labs",
+    subtitle = subtitle,
+    title = title
+  )
   if (!is.null(labs)) {
-    layers[[paste0("l", l)]] <- utils::modifyList(layers[[paste0("l", l)]], labs)
+    layers[[paste0("l", l)]] <- utils::modifyList(
+      layers[[paste0("l", l)]],
+      labs
+    )
   }
 
   layers
diff --git a/R/visualisation_recipe.easycormatrix.R b/R/visualisation_recipe.easycormatrix.R
index 6a24a3ec..a36768b0 100644
--- a/R/visualisation_recipe.easycormatrix.R
+++ b/R/visualisation_recipe.easycormatrix.R
@@ -45,18 +45,20 @@
 #' plot(layers) + theme_modern()
 #' }
 #' @export
-visualisation_recipe.easycormatrix <- function(x,
-                                               show_data = "tile",
-                                               show_text = "text",
-                                               show_legend = TRUE,
-                                               tile = NULL,
-                                               point = NULL,
-                                               text = NULL,
-                                               scale = NULL,
-                                               scale_fill = NULL,
-                                               labs = NULL,
-                                               type = show_data,
-                                               ...) {
+visualisation_recipe.easycormatrix <- function(
+  x,
+  show_data = "tile",
+  show_text = "text",
+  show_legend = TRUE,
+  tile = NULL,
+  point = NULL,
+  text = NULL,
+  scale = NULL,
+  scale_fill = NULL,
+  labs = NULL,
+  type = show_data,
+  ...
+) {
   # handle alias
   if (!missing(type)) {
     show_data <- type
@@ -95,7 +97,8 @@ visualisation_recipe.easycormatrix <- function(x,
   colnames <- names(x)[names(x) != "Parameter1"]
 
   # Reshape to long
-  data <- datawizard::reshape_longer(x,
+  data <- datawizard::reshape_longer(
+    x,
     select = colnames,
     names_to = "Parameter2",
     values_to = "r"
@@ -105,7 +108,8 @@ visualisation_recipe.easycormatrix <- function(x,
   if (is.null(data_text)) {
     data$Text <- paste0(insight::format_value(data$r, zap_small = TRUE))
   } else {
-    temp <- datawizard::reshape_longer(data_text,
+    temp <- datawizard::reshape_longer(
+      data_text,
       select = colnames,
       names_to = "Parameter2",
       values_to = "Text"
@@ -146,7 +150,8 @@ visualisation_recipe.easycormatrix <- function(x,
     if (isTRUE(show_data) || show_data %in% c("tile", "tiles")) {
       layers[[paste0("l", l)]] <- .visualisation_easycormatrix_data(
         type = "tile",
-        data, x = "Parameter2",
+        data,
+        x = "Parameter2",
         y = "Parameter1",
         fill = "r",
         args = tile,
@@ -167,7 +172,6 @@ visualisation_recipe.easycormatrix <- function(x,
     l <- l + 1
   }
 
-
   # Add text
   if (!is.null(show_text) && !isFALSE(show_text)) {
     layers[[paste0("l", l)]] <- .visualisation_easycormatrix_text(
@@ -201,12 +205,21 @@ visualisation_recipe.easycormatrix <- function(x,
 
   # Origin at 0
   if (!is.null(show_data) && show_data %in% c("tile", "tiles")) {
-    layers[[paste0("l", l)]] <- .visualisation_easycormatrix_scale(which = "x_discrete", scale = scale)
+    layers[[paste0("l", l)]] <- .visualisation_easycormatrix_scale(
+      which = "x_discrete",
+      scale = scale
+    )
     l <- l + 1
-    layers[[paste0("l", l)]] <- .visualisation_easycormatrix_scale(which = "y_discrete", scale = scale)
+    layers[[paste0("l", l)]] <- .visualisation_easycormatrix_scale(
+      which = "y_discrete",
+      scale = scale
+    )
     l <- l + 1
   } else if (show_data %in% c("point", "points")) {
-    layers[[paste0("l", l)]] <- .visualisation_easycormatrix_scale(which = "size", scale = scale)
+    layers[[paste0("l", l)]] <- .visualisation_easycormatrix_scale(
+      which = "size",
+      scale = scale
+    )
     l <- l + 1
   }
 
@@ -214,7 +227,11 @@ visualisation_recipe.easycormatrix <- function(x,
   layers[[paste0("l", l)]] <- .visualisation_easycormatrix_labs(labs)
 
   # Out
-  class(layers) <- c("visualisation_recipe", "see_visualisation_recipe", class(layers))
+  class(layers) <- c(
+    "visualisation_recipe",
+    "see_visualisation_recipe",
+    class(layers)
+  )
   attr(layers, "data") <- data
   layers
 }
@@ -222,14 +239,16 @@ visualisation_recipe.easycormatrix <- function(x,
 
 # Layer - Data -------------------------------------------------------------
 
-.visualisation_easycormatrix_data <- function(type = "tile",
-                                              data,
-                                              x,
-                                              y,
-                                              fill,
-                                              args = NULL,
-                                              dot_args = NULL,
-                                              abs_fill = NULL) {
+.visualisation_easycormatrix_data <- function(
+  type = "tile",
+  data,
+  x,
+  y,
+  fill,
+  args = NULL,
+  dot_args = NULL,
+  abs_fill = NULL
+) {
   out <- list(
     geom = type,
     data = data,
@@ -258,14 +277,21 @@ visualisation_recipe.easycormatrix <- function(x,
   } else {
     out$aes$fill <- fill
   }
-  if (!is.null(args)) out <- utils::modifyList(out, args) # Update with additional args
+  if (!is.null(args)) {
+    out <- utils::modifyList(out, args)
+  } # Update with additional args
   out
 }
 
 
 # Layer - Scale Fill -------------------------------------------------------------
 
-.visualisation_easycormatrix_scale_fill <- function(type = "fill", data, scale_fill = NULL, show_legend = TRUE) {
+.visualisation_easycormatrix_scale_fill <- function(
+  type = "fill",
+  data,
+  scale_fill = NULL,
+  show_legend = TRUE
+) {
   low_lim <- ifelse(min(data$r, na.rm = TRUE) < 0, -1, 0)
   high_lim <- ifelse(max(data$r, na.rm = TRUE) > 0, 1, 0)
 
@@ -281,14 +307,23 @@ visualisation_recipe.easycormatrix <- function(x,
     name = "Correlation",
     guide = ifelse(isFALSE(show_legend), "none", "legend")
   )
-  if (!is.null(scale_fill)) out <- utils::modifyList(out, scale_fill) # Update with additional args
+  if (!is.null(scale_fill)) {
+    out <- utils::modifyList(out, scale_fill)
+  } # Update with additional args
   out
 }
 
 
 # Layer - Text -------------------------------------------------------------
 
-.visualisation_easycormatrix_text <- function(data, x, y, label, show_text = "text", text = NULL) {
+.visualisation_easycormatrix_text <- function(
+  data,
+  x,
+  y,
+  label,
+  show_text = "text",
+  text = NULL
+) {
   out <- list(
     geom = show_text,
     data = data,
@@ -298,15 +333,19 @@ visualisation_recipe.easycormatrix <- function(x,
       label = label
     )
   )
-  if (!is.null(text)) out <- utils::modifyList(out, text) # Update with additional args
+  if (!is.null(text)) {
+    out <- utils::modifyList(out, text)
+  } # Update with additional args
   out
 }
 
 
 # Layer - Scale -------------------------------------------------------------------
 
-
-.visualisation_easycormatrix_scale <- function(which = "x_discrete", scale = NULL) {
+.visualisation_easycormatrix_scale <- function(
+  which = "x_discrete",
+  scale = NULL
+) {
   if (which == "size") {
     out <- list(
       geom = "scale_size",
@@ -319,14 +358,15 @@ visualisation_recipe.easycormatrix <- function(x,
     )
   }
 
-  if (!is.null(scale)) out <- utils::modifyList(out, scale) # Update with additional args
+  if (!is.null(scale)) {
+    out <- utils::modifyList(out, scale)
+  } # Update with additional args
   out
 }
 
 
 # Layer - Labs -------------------------------------------------------------------
 
-
 .visualisation_easycormatrix_labs <- function(labs = NULL) {
   out <- list(
     geom = "labs",
@@ -334,6 +374,8 @@ visualisation_recipe.easycormatrix <- function(x,
     y = NULL,
     title = "Correlation Matrix"
   )
-  if (!is.null(labs)) out <- utils::modifyList(out, labs) # Update with additional args
+  if (!is.null(labs)) {
+    out <- utils::modifyList(out, labs)
+  } # Update with additional args
   out
 }
diff --git a/R/visualisation_recipe.easycorrelation.R b/R/visualisation_recipe.easycorrelation.R
index d80dc779..2646fd0f 100644
--- a/R/visualisation_recipe.easycorrelation.R
+++ b/R/visualisation_recipe.easycorrelation.R
@@ -26,13 +26,18 @@ visualisation_recipe.easycorrelation <- function(x, ...) {
   layers[["l2"]] <- list(geom = "ggraph::geom_node_point", size = 22)
   layers[["l3"]] <- list(
     geom = "ggraph::geom_node_text",
-    aes = list(label = "name"), colour = "white"
+    aes = list(label = "name"),
+    colour = "white"
   )
   layers[["l4"]] <- list(geom = "ggraph::theme_graph", base_family = "sans")
   layers[["l5"]] <- list(geom = "guides", edge_width = "none")
 
   # Out
-  class(layers) <- c("visualisation_recipe", "see_visualisation_recipe", class(layers))
+  class(layers) <- c(
+    "visualisation_recipe",
+    "see_visualisation_recipe",
+    class(layers)
+  )
   attr(layers, "data") <- data
   attr(layers, "layout") <- "kk"
   attr(layers, "ggraph") <- TRUE
diff --git a/WIP/spcor_to_cor.R b/WIP/spcor_to_cor.R
index 90a7dd45..2745bbe2 100644
--- a/WIP/spcor_to_cor.R
+++ b/WIP/spcor_to_cor.R
@@ -1,6 +1,11 @@
 #' @rdname cor_to_pcor
 #' @export
-spcor_to_cor <- function(spcor = NULL, cov = NULL, semi = FALSE, tol = .Machine$double.eps^(2 / 3)) {
+spcor_to_cor <- function(
+  spcor = NULL,
+  cov = NULL,
+  semi = FALSE,
+  tol = .Machine$double.eps^(2 / 3)
+) {
   # Get cor
   spcor <- .get_cor(spcor, cov)
 
@@ -8,7 +13,9 @@ spcor_to_cor <- function(spcor = NULL, cov = NULL, semi = FALSE, tol = .Machine$
   m <- -spcor
   diag(m) <- -diag(m)
 
-  stop("Cannot convert semi-partial correlations to correlations yet. We need help for that.")
+  stop(
+    "Cannot convert semi-partial correlations to correlations yet. We need help for that."
+  )
   # if(is.null(cov)){
   #   stop("Covariance matrix (or vector of SD of variables) needs to be passed for semi-partial correlations.")
   # } else{
diff --git a/WIP/utils_bootstrapping.R b/WIP/utils_bootstrapping.R
index 592bc824..6d55f583 100644
--- a/WIP/utils_bootstrapping.R
+++ b/WIP/utils_bootstrapping.R
@@ -4,15 +4,15 @@
   strap <- replicate(n, .resample(data), simplify = FALSE)
 
   # add resample ID, may be used for other functions
-  for (i in seq_len(length(strap))) strap[[i]]$Resample_id <- i
+  for (i in seq_len(length(strap))) {
+    strap[[i]]$Resample_id <- i
+  }
 
   # return as list variable
   data.frame(bootstraps = I(strap))
 }
 
 
-
-
 #' @keywords internal
 .resample <- function(data) {
   structure(
@@ -25,11 +25,14 @@
 }
 
 
-
-
 #' @importFrom stats qt sd quantile na.omit
 #' @keywords internal
-.bootstrapped_ci <- function(data, select = NULL, method = c("normal", "quantile"), ci.lvl = .95) {
+.bootstrapped_ci <- function(
+  data,
+  select = NULL,
+  method = c("normal", "quantile"),
+  ci.lvl = .95
+) {
   # match arguments
   method <- match.arg(method)
 
@@ -45,7 +48,11 @@
     # bootstrap values or quantiles
     if (method == "normal") {
       # get bootstrap standard error
-      bootse <- stats::qt((1 + ci.lvl) / 2, df = length(stats::na.omit(x)) - 1) * stats::sd(x, na.rm = T)
+      bootse <- stats::qt(
+        (1 + ci.lvl) / 2,
+        df = length(stats::na.omit(x)) - 1
+      ) *
+        stats::sd(x, na.rm = T)
       # lower and upper confidence interval
       ci <- mean(x, na.rm = T) + c(-bootse, bootse)
     } else {
@@ -59,16 +66,12 @@
 }
 
 
-
-
 #' @keywords internal
 .transform_boot_result <- function(result) {
   as.data.frame(t(as.data.frame(result)))
 }
 
 
-
-
 #' @keywords internal
 as.data.frame.correlation_resample <- function(x, ...) {
   x$data[x$id, , drop = FALSE]
diff --git a/WIP/utils_reorder_matrix.R b/WIP/utils_reorder_matrix.R
index 9b3b92ff..543306d7 100644
--- a/WIP/utils_reorder_matrix.R
+++ b/WIP/utils_reorder_matrix.R
@@ -14,7 +14,11 @@
     stop("Matrix must be squared to be re-arranged.")
   }
 
-  reorder_distance <- stats::as.dist((1 - reorder_distance) / 2, diag = TRUE, upper = TRUE)
+  reorder_distance <- stats::as.dist(
+    (1 - reorder_distance) / 2,
+    diag = TRUE,
+    upper = TRUE
+  )
   hc <- stats::hclust(reorder_distance, method = method)
   x <- x[hc$order, hc$order]
   x
diff --git a/air.toml b/air.toml
new file mode 100644
index 00000000..8e08bdbb
--- /dev/null
+++ b/air.toml
@@ -0,0 +1,7 @@
+[format]
+line-width = 80
+indent-width = 2
+indent-style = "space"
+line-ending = "lf"
+persistent-line-breaks = true
+skip = ["tribble"]
\ No newline at end of file
diff --git a/paper/make_figures.R b/paper/make_figures.R
index b3cdbf61..8a57b6ab 100644
--- a/paper/make_figures.R
+++ b/paper/make_figures.R
@@ -3,7 +3,6 @@ library(correlation)
 library(see)
 
 
-
 # Figure 1 ----------------------------------------------------------------
 
 # Generate data
@@ -15,7 +14,11 @@ data <- arrange(data, V2)
 
 # Outliers
 data$V2[c(150, 185)] <- c(max(data$V2) * 1, max(data$V2) * 1)
-data$V2[c(1, 5, 10)] <- c(min(data$V2) * 2, max(data$V2) * 1, min(data$V2) * 1.5)
+data$V2[c(1, 5, 10)] <- c(
+  min(data$V2) * 2,
+  max(data$V2) * 1,
+  min(data$V2) * 1.5
+)
 
 # Rescale to match coef
 data$V2 <- datawizard::data_rescale(data$V2, to = c(0, 1))
@@ -23,14 +26,64 @@ data$V2 <- datawizard::data_rescale(data$V2, to = c(0, 1))
 
 # Correlation results
 rez <- rbind(
-  select(cor_test(data, "V1", "V2", method = "Pearson"), r, CI_low, CI_high, Method),
-  select(cor_test(data, "V1", "V2", method = "Spearman"), r = rho, CI_low, CI_high, Method),
-  select(cor_test(data, "V1", "V2", method = "Kendall"), r = tau, CI_low, CI_high, Method),
-  select(cor_test(data, "V1", "V2", method = "biweight"), r, CI_low, CI_high, Method),
-  select(cor_test(data, "V1", "V2", method = "percentage"), r, CI_low, CI_high, Method),
-  select(cor_test(data, "V1", "V2", method = "distance", corrected = FALSE), r, CI_low, CI_high, Method),
-  select(cor_test(data, "V1", "V2", method = "shepherd"), r = rho, CI_low, CI_high, Method),
-  mutate(select(cor_test(data, "V1", "V2", method = "Pearson", bayesian = TRUE), r = rho, CI_low, CI_high), Method = "Bayesian")
+  select(
+    cor_test(data, "V1", "V2", method = "Pearson"),
+    r,
+    CI_low,
+    CI_high,
+    Method
+  ),
+  select(
+    cor_test(data, "V1", "V2", method = "Spearman"),
+    r = rho,
+    CI_low,
+    CI_high,
+    Method
+  ),
+  select(
+    cor_test(data, "V1", "V2", method = "Kendall"),
+    r = tau,
+    CI_low,
+    CI_high,
+    Method
+  ),
+  select(
+    cor_test(data, "V1", "V2", method = "biweight"),
+    r,
+    CI_low,
+    CI_high,
+    Method
+  ),
+  select(
+    cor_test(data, "V1", "V2", method = "percentage"),
+    r,
+    CI_low,
+    CI_high,
+    Method
+  ),
+  select(
+    cor_test(data, "V1", "V2", method = "distance", corrected = FALSE),
+    r,
+    CI_low,
+    CI_high,
+    Method
+  ),
+  select(
+    cor_test(data, "V1", "V2", method = "shepherd"),
+    r = rho,
+    CI_low,
+    CI_high,
+    Method
+  ),
+  mutate(
+    select(
+      cor_test(data, "V1", "V2", method = "Pearson", bayesian = TRUE),
+      r = rho,
+      CI_low,
+      CI_high
+    ),
+    Method = "Bayesian"
+  )
 )
 
 # Format correlation to match data input from scatter
@@ -39,7 +92,10 @@ rez <- rez %>%
   mutate(
     Method = forcats::fct_reorder(as.factor(Method), r),
     V2 = r,
-    x = stringr::str_remove_all(levels(ggplot2::cut_interval(data$V1, n = n())), "[\\(\\[\\]]")
+    x = stringr::str_remove_all(
+      levels(ggplot2::cut_interval(data$V1, n = n())),
+      "[\\(\\[\\]]"
+    )
   ) %>%
   separate(x, into = c("low", "high"), sep = ",") %>%
   mutate(V1 = (as.numeric(high) + as.numeric(low)) / 2)
@@ -48,7 +104,6 @@ rez <- rez %>%
 # rez[rez$Method=="Spearman", c("CI_low", "CI_high")] <- rep(rez[rez$Method=="Spearman", "r"], 2)
 # rez[rez$Method=="Kendall", c("CI_low", "CI_high")] <- rep(rez[rez$Method=="Kendall", "r"], 2)
 
-
 # Initialize plot
 fig1 <- ggplot(data, aes(x = V1, y = V2)) +
   see::theme_modern() +
@@ -60,7 +115,13 @@ fig1 <- ggplot(data, aes(x = V1, y = V2)) +
   scale_colour_material_d("rainbow") +
 
   # rez plot
-  geom_segment(data = rez, aes(xend = V1, yend = -Inf, colour = Method), size = 20, alpha = 0.6, key_glyph = "point") +
+  geom_segment(
+    data = rez,
+    aes(xend = V1, yend = -Inf, colour = Method),
+    size = 20,
+    alpha = 0.6,
+    key_glyph = "point"
+  ) +
   # geom_bar(data=rez, aes(fill=Method), stat = "identity")
   geom_errorbar(
     data = rez,
@@ -81,14 +142,6 @@ fig1
 ggsave("figure1.png", fig1, height = 6, width = see::golden_ratio(6), dpi = 300)
 
 
-
-
-
-
-
-
-
-
 # Figure 2 ----------------------------------------------------------------
 
 fig2 <- correlation(iris) %>%
diff --git a/tests/testthat/helper.R b/tests/testthat/helper.R
index 0c5a12bd..afff3ed1 100644
--- a/tests/testthat/helper.R
+++ b/tests/testthat/helper.R
@@ -1,6 +1,11 @@
 skip_if_not_or_load_if_installed <- function(package, minimum_version = NULL) {
   testthat::skip_if_not_installed(package, minimum_version = minimum_version)
   suppressMessages(suppressWarnings(suppressPackageStartupMessages(
-    require(package, warn.conflicts = FALSE, character.only = TRUE, quietly = TRUE)
+    require(
+      package,
+      warn.conflicts = FALSE,
+      character.only = TRUE,
+      quietly = TRUE
+    )
   )))
 }
diff --git a/tests/testthat/test-cor_multilevel.R b/tests/testthat/test-cor_multilevel.R
index 348acfc1..bae30d8a 100644
--- a/tests/testthat/test-cor_multilevel.R
+++ b/tests/testthat/test-cor_multilevel.R
@@ -2,10 +2,21 @@ test_that("comparison rmcorr", {
   skip_if_not_or_load_if_installed("lme4")
   skip_if_not_or_load_if_installed("rmcorr")
   set.seed(123)
-  rez_rmcorr <- rmcorr::rmcorr(Species, Sepal.Length, Sepal.Width, dataset = iris)
+  rez_rmcorr <- rmcorr::rmcorr(
+    Species,
+    Sepal.Length,
+    Sepal.Width,
+    dataset = iris
+  )
 
   set.seed(123)
-  rez <- cor_test(iris[c(1, 2, 5)], "Sepal.Length", "Sepal.Width", partial = TRUE, multilevel = TRUE)
+  rez <- cor_test(
+    iris[c(1, 2, 5)],
+    "Sepal.Length",
+    "Sepal.Width",
+    partial = TRUE,
+    multilevel = TRUE
+  )
 
   expect_equal(rez$r, rez_rmcorr$r, tolerance = 0.001)
   expect_equal(rez$p, rez_rmcorr$p, tolerance = 0.001)
@@ -19,9 +30,15 @@ test_that("Reductio ad absurdum", {
   skip_if_not_or_load_if_installed("lme4")
   cormatrix <- matrix(
     c(
-      1.0, 0.3, 0.6,
-      0.3, 1.0, 0.0,
-      0.6, 0.0, 1.0
+      1.0,
+      0.3,
+      0.6,
+      0.3,
+      1.0,
+      0.0,
+      0.6,
+      0.0,
+      1.0
     ),
     nrow = 3
   )
@@ -36,6 +53,15 @@ test_that("Reductio ad absurdum", {
   rez <- suppressMessages(correlation(data, multilevel = TRUE, verbose = FALSE))
   expect_equal(max(as.matrix(rez) - cormatrix), 0, tolerance = 0.01)
 
-  rez <- suppressMessages(correlation(data, multilevel = TRUE, partial = TRUE, verbose = FALSE))
-  expect_equal(max(as.matrix(pcor_to_cor(rez)) - cormatrix), 0, tolerance = 0.01)
+  rez <- suppressMessages(correlation(
+    data,
+    multilevel = TRUE,
+    partial = TRUE,
+    verbose = FALSE
+  ))
+  expect_equal(
+    max(as.matrix(pcor_to_cor(rez)) - cormatrix),
+    0,
+    tolerance = 0.01
+  )
 })
diff --git a/tests/testthat/test-cor_sort.R b/tests/testthat/test-cor_sort.R
index 61886466..50271a58 100644
--- a/tests/testthat/test-cor_sort.R
+++ b/tests/testthat/test-cor_sort.R
@@ -19,7 +19,10 @@ test_that("cor_sort", {
 
   r2sort <- cor_sort(r2)
   expect_false(all(rownames(r2sort) == names(mtcars)[1:5]))
-  expect_identical(colnames(r2sort), c("am", "gear", "qsec", "vs", "wt", "carb"))
+  expect_identical(
+    colnames(r2sort),
+    c("am", "gear", "qsec", "vs", "wt", "carb")
+  )
   expect_identical(rownames(r2sort), c("drat", "disp", "hp", "cyl", "mpg"))
   # heatmap(r2sort, Rowv = NA, Colv = NA)  # visualize
 
@@ -30,7 +33,10 @@ test_that("cor_sort", {
   expect_false(all(rez1$Parameter1 == rez1sort$Parameter1))
 
   # Non-square
-  rez2 <- correlation::correlation(mtcars[names(mtcars)[1:5]], mtcars[names(mtcars)[6:11]])
+  rez2 <- correlation::correlation(
+    mtcars[names(mtcars)[1:5]],
+    mtcars[names(mtcars)[6:11]]
+  )
   rez2sort <- cor_sort(rez2)
   expect_false(all(rez2$Parameter1 == rez2sort$Parameter1))
 
diff --git a/tests/testthat/test-cor_test.R b/tests/testthat/test-cor_test.R
index 36e9f920..501c03d0 100644
--- a/tests/testthat/test-cor_test.R
+++ b/tests/testthat/test-cor_test.R
@@ -7,7 +7,11 @@ test_that("cor_test frequentist", {
 
 test_that("cor_test kendall", {
   out <- cor_test(iris, "Petal.Length", "Petal.Width", method = "kendall")
-  out2 <- stats::cor.test(iris$Petal.Length, iris$Petal.Width, method = "kendall")
+  out2 <- stats::cor.test(
+    iris$Petal.Length,
+    iris$Petal.Width,
+    method = "kendall"
+  )
 
   expect_equal(out$tau, out2$estimate[[1]], tolerance = 0.001)
   expect_equal(out$p, out2$p.value[[1]], tolerance = 0.001)
@@ -23,15 +27,32 @@ test_that("cor_test bayesian", {
   df_1 <- cor_test(iris, "Petal.Length", "Petal.Width", bayesian = TRUE)
 
   set.seed(123)
-  df_2 <- cor_test(iris, "Petal.Length", "Petal.Width", method = "auto", bayesian = TRUE)
+  df_2 <- cor_test(
+    iris,
+    "Petal.Length",
+    "Petal.Width",
+    method = "auto",
+    bayesian = TRUE
+  )
   expect_equal(df_1, df_2, tolerance = 0.001)
 
-  out2 <- cor_test(iris, "Petal.Length", "Petal.Width", method = "spearman", bayesian = TRUE)
+  out2 <- cor_test(
+    iris,
+    "Petal.Length",
+    "Petal.Width",
+    method = "spearman",
+    bayesian = TRUE
+  )
   expect_equal(out2$rho, 0.9323004, tolerance = 0.01)
 
   df <- iris
   df$Petal.Length2 <- df$Petal.Length
-  out3 <- suppressWarnings(cor_test(df, "Petal.Length", "Petal.Length2", bayesian = TRUE))
+  out3 <- suppressWarnings(cor_test(
+    df,
+    "Petal.Length",
+    "Petal.Length2",
+    bayesian = TRUE
+  ))
   expect_equal(out3$rho, 1.000, tolerance = 0.01)
 
   if (getRversion() >= "3.6") {
@@ -45,14 +66,62 @@ test_that("cor_test bayesian", {
   }
 
   # unsupported
-  expect_error(cor_test(mtcars, "wt", "mpg", method = "biserial", bayesian = TRUE))
-  expect_error(cor_test(mtcars, "wt", "mpg", method = "polychoric", bayesian = TRUE))
-  expect_error(cor_test(mtcars, "wt", "mpg", method = "tetrachoric", bayesian = TRUE))
-  expect_error(cor_test(mtcars, "wt", "mpg", method = "biweight", bayesian = TRUE))
-  expect_error(cor_test(mtcars, "wt", "mpg", method = "distance", bayesian = TRUE))
-  expect_error(cor_test(mtcars, "wt", "mpg", method = "percentage", bayesian = TRUE))
-  expect_error(cor_test(mtcars, "wt", "mpg", method = "blomqvist", bayesian = TRUE))
-  expect_error(cor_test(mtcars, "wt", "mpg", method = "hoeffding", bayesian = TRUE))
+  expect_error(cor_test(
+    mtcars,
+    "wt",
+    "mpg",
+    method = "biserial",
+    bayesian = TRUE
+  ))
+  expect_error(cor_test(
+    mtcars,
+    "wt",
+    "mpg",
+    method = "polychoric",
+    bayesian = TRUE
+  ))
+  expect_error(cor_test(
+    mtcars,
+    "wt",
+    "mpg",
+    method = "tetrachoric",
+    bayesian = TRUE
+  ))
+  expect_error(cor_test(
+    mtcars,
+    "wt",
+    "mpg",
+    method = "biweight",
+    bayesian = TRUE
+  ))
+  expect_error(cor_test(
+    mtcars,
+    "wt",
+    "mpg",
+    method = "distance",
+    bayesian = TRUE
+  ))
+  expect_error(cor_test(
+    mtcars,
+    "wt",
+    "mpg",
+    method = "percentage",
+    bayesian = TRUE
+  ))
+  expect_error(cor_test(
+    mtcars,
+    "wt",
+    "mpg",
+    method = "blomqvist",
+    bayesian = TRUE
+  ))
+  expect_error(cor_test(
+    mtcars,
+    "wt",
+    "mpg",
+    method = "hoeffding",
+    bayesian = TRUE
+  ))
   expect_error(cor_test(mtcars, "wt", "mpg", method = "gamma", bayesian = TRUE))
 })
 
@@ -64,12 +133,22 @@ test_that("cor_test tetrachoric", {
   data$Petal.Width_binary <- as.numeric(data$Petal.Width > 1.2)
 
   # With Factors / Binary
-  out <- cor_test(data, "Sepal.Width_binary", "Petal.Width_binary", method = "tetrachoric")
+  out <- cor_test(
+    data,
+    "Sepal.Width_binary",
+    "Petal.Width_binary",
+    method = "tetrachoric"
+  )
   expect_equal(out$rho, -0.526, tolerance = 0.01)
 
   data$Petal.Width_ordinal <- as.factor(round(data$Petal.Width))
   data$Sepal.Length_ordinal <- as.factor(round(data$Sepal.Length))
-  out <- cor_test(data, "Petal.Width_ordinal", "Sepal.Length_ordinal", method = "polychoric")
+  out <- cor_test(
+    data,
+    "Petal.Width_ordinal",
+    "Sepal.Length_ordinal",
+    method = "polychoric"
+  )
 
   # Curently CRAN checks show two possible results for this:
   if (isTRUE(all.equal(out$rho, 0.7507764, tolerance = 0.1))) {
@@ -78,16 +157,34 @@ test_that("cor_test tetrachoric", {
     expect_equal(out$rho, 0.528, tolerance = 0.01)
   }
 
-  out <- cor_test(data, "Sepal.Width", "Sepal.Length_ordinal", method = "polychoric")
+  out <- cor_test(
+    data,
+    "Sepal.Width",
+    "Sepal.Length_ordinal",
+    method = "polychoric"
+  )
   expect_equal(out$rho, -0.144, tolerance = 0.01)
 
   # Biserial
-  out <- cor_test(data, "Sepal.Width", "Petal.Width_binary", method = "pointbiserial")
+  out <- cor_test(
+    data,
+    "Sepal.Width",
+    "Petal.Width_binary",
+    method = "pointbiserial"
+  )
   expect_equal(out$rho, -0.3212561, tolerance = 0.01)
 
-  out <- cor_test(data, "Sepal.Width", "Petal.Width_binary", method = "biserial")
+  out <- cor_test(
+    data,
+    "Sepal.Width",
+    "Petal.Width_binary",
+    method = "biserial"
+  )
   expect_equal(out$rho, -0.403, tolerance = 0.01)
-  out_psych <- psych::biserial(data[["Sepal.Width"]], data[["Petal.Width_binary"]])[1]
+  out_psych <- psych::biserial(
+    data[["Sepal.Width"]],
+    data[["Petal.Width_binary"]]
+  )[1]
 
   set.seed(123)
   n <- 100
@@ -109,8 +206,20 @@ test_that("cor_test tetrachoric", {
 
 
 test_that("cor_test robust", {
-  out1 <- cor_test(iris, "Petal.Length", "Petal.Width", method = "pearson", ranktransform = TRUE)
-  out2 <- cor_test(iris, "Petal.Length", "Petal.Width", method = "spearman", ranktransform = FALSE)
+  out1 <- cor_test(
+    iris,
+    "Petal.Length",
+    "Petal.Width",
+    method = "pearson",
+    ranktransform = TRUE
+  )
+  out2 <- cor_test(
+    iris,
+    "Petal.Length",
+    "Petal.Width",
+    method = "spearman",
+    ranktransform = FALSE
+  )
   expect_equal(out1$r, out2$rho, tolerance = 0.01)
 })
 
@@ -142,7 +251,13 @@ test_that("cor_test shepherd", {
 
   skip_if_not_or_load_if_installed("BayesFactor")
   set.seed(333)
-  out2 <- cor_test(iris, "Petal.Length", "Petal.Width", method = "shepherd", bayesian = TRUE)
+  out2 <- cor_test(
+    iris,
+    "Petal.Length",
+    "Petal.Width",
+    method = "shepherd",
+    bayesian = TRUE
+  )
   expect_equal(out2$rho, 0.9429992, tolerance = 0.01)
 })
 
@@ -180,16 +295,25 @@ test_that("cor_test gaussian", {
   out <- cor_test(iris, "Petal.Length", "Petal.Width", method = "gaussian")
   expect_equal(out$r, 0.87137, tolerance = 0.01)
 
-  out <- cor_test(iris, "Petal.Length", "Petal.Width", method = "gaussian", bayesian = TRUE)
+  out <- cor_test(
+    iris,
+    "Petal.Length",
+    "Petal.Width",
+    method = "gaussian",
+    bayesian = TRUE
+  )
   expect_equal(out$rho, 0.8620878, tolerance = 0.01)
 })
 
 
 # Additional arguments ----------------------------------------------------
 
-
 test_that("cor_test one-sided p value", {
-  baseline <- cor.test(iris$Petal.Length, iris$Petal.Width, alternative = "greater")
+  baseline <- cor.test(
+    iris$Petal.Length,
+    iris$Petal.Width,
+    alternative = "greater"
+  )
 
   out <- cor_test(iris, "Petal.Length", "Petal.Width", alternative = "greater")
   expect_equal(out$p, baseline$p.value, tolerance = 0.000001)
@@ -199,6 +323,9 @@ test_that("cor_test one-sided p value", {
 # Edge cases --------------------------------------------------------------
 
 test_that("cor_test 2 valid observations", {
-  out <- suppressWarnings(correlation(data.frame(v2 = c(2, 1, 1, 2), v3 = c(1, 2, NA, NA))))
+  out <- suppressWarnings(correlation(data.frame(
+    v2 = c(2, 1, 1, 2),
+    v3 = c(1, 2, NA, NA)
+  )))
   expect_true(is.na(out$r))
 })
diff --git a/tests/testthat/test-cor_test_na_present.R b/tests/testthat/test-cor_test_na_present.R
index 8449c265..92d49d1c 100644
--- a/tests/testthat/test-cor_test_na_present.R
+++ b/tests/testthat/test-cor_test_na_present.R
@@ -11,7 +11,11 @@ test_that("cor_test kendall", {
   skip_if_not_or_load_if_installed("ggplot2")
 
   out <- cor_test(ggplot2::msleep, "brainwt", "sleep_rem", method = "kendall")
-  out2 <- suppressWarnings(stats::cor.test(ggplot2::msleep$brainwt, ggplot2::msleep$sleep_rem, method = "kendall"))
+  out2 <- suppressWarnings(stats::cor.test(
+    ggplot2::msleep$brainwt,
+    ggplot2::msleep$sleep_rem,
+    method = "kendall"
+  ))
 
   expect_equal(out$tau, out2$estimate[[1]], tolerance = 0.001)
   expect_equal(out$p, out2$p.value[[1]], tolerance = 0.001)
@@ -35,26 +39,58 @@ test_that("cor_test tetrachoric", {
   data$sleep_rem_binary <- as.numeric(data$sleep_rem > 1.2)
 
   # With Factors / Binary
-  expect_error(cor_test(data, "brainwt_binary", "sleep_rem_binary", method = "tetrachoric"))
+  expect_error(cor_test(
+    data,
+    "brainwt_binary",
+    "sleep_rem_binary",
+    method = "tetrachoric"
+  ))
 
   data$sleep_rem_ordinal <- as.factor(round(data$sleep_rem))
   data$brainwt_ordinal <- as.factor(round(data$brainwt))
 
-  out <- suppressWarnings(cor_test(data, "brainwt", "brainwt_ordinal", method = "polychoric"))
+  out <- suppressWarnings(cor_test(
+    data,
+    "brainwt",
+    "brainwt_ordinal",
+    method = "polychoric"
+  ))
   expect_equal(out$rho, 0.9999, tolerance = 0.01)
 
   # Biserial
-  expect_error(cor_test(data, "brainwt", "sleep_rem_binary", method = "pointbiserial"))
-
-  expect_error(cor_test(data, "brainwt", "sleep_rem_binary", method = "biserial"))
+  expect_error(cor_test(
+    data,
+    "brainwt",
+    "sleep_rem_binary",
+    method = "pointbiserial"
+  ))
+
+  expect_error(cor_test(
+    data,
+    "brainwt",
+    "sleep_rem_binary",
+    method = "biserial"
+  ))
 })
 
 
 test_that("cor_test robust", {
   skip_if_not_or_load_if_installed("ggplot2")
 
-  out1 <- cor_test(ggplot2::msleep, "brainwt", "sleep_rem", method = "pearson", ranktransform = TRUE)
-  out2 <- cor_test(ggplot2::msleep, "brainwt", "sleep_rem", method = "spearman", ranktransform = FALSE)
+  out1 <- cor_test(
+    ggplot2::msleep,
+    "brainwt",
+    "sleep_rem",
+    method = "pearson",
+    ranktransform = TRUE
+  )
+  out2 <- cor_test(
+    ggplot2::msleep,
+    "brainwt",
+    "sleep_rem",
+    method = "spearman",
+    ranktransform = FALSE
+  )
   expect_equal(out1$r, out2$rho, tolerance = 0.01)
 })
 
@@ -75,7 +111,12 @@ test_that("cor_test percentage", {
   skip_if_not_or_load_if_installed("ggplot2")
   skip_if_not_or_load_if_installed("WRS2")
 
-  out <- cor_test(ggplot2::msleep, "brainwt", "sleep_rem", method = "percentage")
+  out <- cor_test(
+    ggplot2::msleep,
+    "brainwt",
+    "sleep_rem",
+    method = "percentage"
+  )
   comparison <- WRS2::pbcor(ggplot2::msleep$brainwt, ggplot2::msleep$sleep_rem)
   expect_equal(out$r, as.numeric(comparison$cor), tolerance = 0.01)
 })
@@ -85,7 +126,12 @@ test_that("cor_test shepherd", {
   skip_if_not_or_load_if_installed("ggplot2")
 
   set.seed(333)
-  expect_error(cor_test(ggplot2::msleep, "brainwt", "sleep_rem", method = "shepherd"))
+  expect_error(cor_test(
+    ggplot2::msleep,
+    "brainwt",
+    "sleep_rem",
+    method = "shepherd"
+  ))
 })
 
 
@@ -121,19 +167,33 @@ test_that("cor_test gaussian", {
   expect_equal(out$r, -0.3679795, tolerance = 0.01)
 
   skip_if_not_or_load_if_installed("BayesFactor")
-  out <- cor_test(ggplot2::msleep, "brainwt", "sleep_rem", method = "gaussian", bayesian = TRUE)
+  out <- cor_test(
+    ggplot2::msleep,
+    "brainwt",
+    "sleep_rem",
+    method = "gaussian",
+    bayesian = TRUE
+  )
   expect_equal(out$rho, -0.3269572, tolerance = 0.01)
 })
 
 
 # Additional arguments ----------------------------------------------------
 
-
 test_that("cor_test one-sided p value", {
   skip_if_not_or_load_if_installed("ggplot2")
 
-  baseline <- cor.test(ggplot2::msleep$brainwt, ggplot2::msleep$sleep_rem, alternative = "greater")
-
-  out <- cor_test(ggplot2::msleep, "brainwt", "sleep_rem", alternative = "greater")
+  baseline <- cor.test(
+    ggplot2::msleep$brainwt,
+    ggplot2::msleep$sleep_rem,
+    alternative = "greater"
+  )
+
+  out <- cor_test(
+    ggplot2::msleep,
+    "brainwt",
+    "sleep_rem",
+    alternative = "greater"
+  )
   expect_equal(out$p, baseline$p.value, tolerance = 0.000001)
 })
diff --git a/tests/testthat/test-cor_to_pcor.R b/tests/testthat/test-cor_to_pcor.R
index a256b14c..21b57512 100644
--- a/tests/testthat/test-cor_to_pcor.R
+++ b/tests/testthat/test-cor_to_pcor.R
@@ -9,7 +9,11 @@ test_that("pcor_to_cor", {
   pcormat <- summary(out, redundant = TRUE)
 
   ppcor <- ppcor::pcor(iris[1:4])
-  expect_equal(max(as.matrix(pcormat[2:5]) - as.matrix(ppcor$estimate)), 0, tolerance = 0.01)
+  expect_equal(
+    max(as.matrix(pcormat[2:5]) - as.matrix(ppcor$estimate)),
+    0,
+    tolerance = 0.01
+  )
 
   # TODO: fix
   # cormat <- pcor_to_cor(pcormat)
@@ -22,9 +26,16 @@ test_that("pcor_to_cor", {
   # expect_equal(mean(p - hmisc$P, na.rm = TRUE), 0, tolerance = 0.001)
 
   # easycorrelation
-  cormat <- summary(pcor_to_cor(correlation(iris, partial = TRUE)), redundant = TRUE)
-
-  expect_equal(max(as.matrix(cormat[2:5]) - as.matrix(cor(iris[1:4]))), 0, tolerance = 0.01)
+  cormat <- summary(
+    pcor_to_cor(correlation(iris, partial = TRUE)),
+    redundant = TRUE
+  )
+
+  expect_equal(
+    max(as.matrix(cormat[2:5]) - as.matrix(cor(iris[1:4]))),
+    0,
+    tolerance = 0.01
+  )
 
   hmisc <- Hmisc::rcorr(as.matrix(iris[1:4]), type = c("pearson"))
   expect_equal(mean(as.matrix(cormat[2:5]) - hmisc$r), 0, tolerance = 0.0001)
diff --git a/tests/testthat/test-cormatrix_to_excel.R b/tests/testthat/test-cormatrix_to_excel.R
index 9fa24001..20afd229 100644
--- a/tests/testthat/test-cormatrix_to_excel.R
+++ b/tests/testthat/test-cormatrix_to_excel.R
@@ -1,6 +1,7 @@
 test_that("cormatrix_to_excel select", {
   skip_if_not_or_load_if_installed("openxlsx2")
-  expect_snapshot(suppressWarnings(cormatrix_to_excel(mtcars,
+  expect_snapshot(suppressWarnings(cormatrix_to_excel(
+    mtcars,
     filename = "cormatrix1",
     overwrite = TRUE,
     p_adjust = "none",
@@ -13,7 +14,8 @@ test_that("cormatrix_to_excel select", {
 
 test_that("cormatrix_to_excel p_adjust", {
   skip_if_not_or_load_if_installed("openxlsx2")
-  expect_snapshot(suppressWarnings(cormatrix_to_excel(airquality,
+  expect_snapshot(suppressWarnings(cormatrix_to_excel(
+    airquality,
     filename = "cormatrix1",
     overwrite = FALSE,
     p_adjust = "holm",
diff --git a/tests/testthat/test-correlation.R b/tests/testthat/test-correlation.R
index 9dc59c56..395aa67f 100644
--- a/tests/testthat/test-correlation.R
+++ b/tests/testthat/test-correlation.R
@@ -19,13 +19,16 @@ test_that("comparison with other packages", {
   p <- as.matrix(attributes(rez)$p[2:5])
   expect_equal(mean(p - hmisc$P, na.rm = TRUE), 0, tolerance = 0.0001)
 
-
   # Spearman
   out <- correlation(iris, include_factors = FALSE, method = "spearman")
   rez <- as.data.frame(summary(out, redundant = TRUE))
 
   r <- as.matrix(rez[2:5])
-  expect_equal(mean(r - cor(iris[1:4], method = "spearman")), 0, tolerance = 0.0001)
+  expect_equal(
+    mean(r - cor(iris[1:4], method = "spearman")),
+    0,
+    tolerance = 0.0001
+  )
 
   hmisc <- Hmisc::rcorr(as.matrix(iris[1:4]), type = c("spearman"))
   expect_equal(mean(r - hmisc$r), 0, tolerance = 0.0001)
@@ -38,7 +41,11 @@ test_that("comparison with other packages", {
   rez <- as.data.frame(summary(out, redundant = TRUE))
 
   r <- as.matrix(rez[2:5])
-  expect_equal(mean(r - cor(iris[1:4], method = "kendall")), 0, tolerance = 0.0001)
+  expect_equal(
+    mean(r - cor(iris[1:4], method = "kendall")),
+    0,
+    tolerance = 0.0001
+  )
 
   # Biweight
   out <- correlation(iris, include_factors = FALSE, method = "biweight")
@@ -53,7 +60,12 @@ test_that("comparison with other packages", {
   expect_equal(mean(r - cor(iris[1:2], iris[3:4])), 0, tolerance = 0.0001)
 
   # Partial
-  out <- correlation(mtcars, include_factors = FALSE, partial = TRUE, p_adjust = "none")
+  out <- correlation(
+    mtcars,
+    include_factors = FALSE,
+    partial = TRUE,
+    p_adjust = "none"
+  )
   rez <- as.data.frame(summary(out, redundant = TRUE))
 
   r <- as.matrix(rez[2:ncol(rez)])
@@ -77,9 +89,13 @@ test_that("comparison with other packages", {
   p <- bayestestR::pd_to_p(pd)
   expect_equal(mean(p - hmisc$P, na.rm = TRUE), 0, tolerance = 0.01)
 
-
   # Bayesian - Partial
-  out <- correlation(iris, include_factors = FALSE, bayesian = TRUE, partial = TRUE)
+  out <- correlation(
+    iris,
+    include_factors = FALSE,
+    bayesian = TRUE,
+    partial = TRUE
+  )
   rez <- as.data.frame(summary(out, redundant = TRUE))
 
   r <- as.matrix(rez[2:5])
@@ -90,9 +106,14 @@ test_that("comparison with other packages", {
   p <- bayestestR::pd_to_p(pd)
   expect_equal(mean(abs(p - as.matrix(ppcor$p.value))), 0, tolerance = 0.001)
 
-
   # Bayesian (Full) - Partial
-  out <- correlation(iris, include_factors = FALSE, bayesian = TRUE, partial = TRUE, partial_bayesian = TRUE)
+  out <- correlation(
+    iris,
+    include_factors = FALSE,
+    bayesian = TRUE,
+    partial = TRUE,
+    partial_bayesian = TRUE
+  )
   rez <- as.data.frame(summary(out, redundant = TRUE))
 
   r <- as.matrix(rez[2:5])
@@ -106,22 +127,47 @@ test_that("format checks", {
   skip_if_not_or_load_if_installed("psych")
 
   out <- correlation(iris, include_factors = TRUE)
-  expect_identical(c(nrow(summary(out, redundant = TRUE)), ncol(summary(out, redundant = TRUE))), c(7L, 8L))
+  expect_identical(
+    c(
+      nrow(summary(out, redundant = TRUE)),
+      ncol(summary(out, redundant = TRUE))
+    ),
+    c(7L, 8L)
+  )
   expect_identical(c(nrow(summary(out)), ncol(summary(out))), c(6L, 7L))
 
   expect_message(
     out <- correlation(iris, method = "auto", include_factors = TRUE),
     "Check your data"
   )
-  expect_identical(c(nrow(summary(out, redundant = TRUE)), ncol(summary(out, redundant = TRUE))), c(7L, 8L))
+  expect_identical(
+    c(
+      nrow(summary(out, redundant = TRUE)),
+      ncol(summary(out, redundant = TRUE))
+    ),
+    c(7L, 8L)
+  )
   expect_identical(c(nrow(summary(out)), ncol(summary(out))), c(6L, 7L))
 
-  expect_true(all(c("Pearson correlation", "Point-biserial correlation", "Tetrachoric correlation") %in% out$Method))
+  expect_true(all(
+    c(
+      "Pearson correlation",
+      "Point-biserial correlation",
+      "Tetrachoric correlation"
+    ) %in%
+      out$Method
+  ))
 
   # X and Y
   out <- correlation(iris[1:2], iris[3:4])
   expect_identical(c(nrow(out), ncol(out)), c(4L, 11L))
-  expect_identical(c(nrow(summary(out, redundant = TRUE)), ncol(summary(out, redundant = TRUE))), c(2L, 3L))
+  expect_identical(
+    c(
+      nrow(summary(out, redundant = TRUE)),
+      ncol(summary(out, redundant = TRUE))
+    ),
+    c(2L, 3L)
+  )
   expect_identical(c(nrow(summary(out)), ncol(summary(out))), c(2L, 3L))
 
   # Grouped
@@ -132,7 +178,13 @@ test_that("format checks", {
     group_by(Species) %>%
     correlation(include_factors = TRUE)
   expect_identical(c(nrow(out), ncol(out)), c(18L, 12L))
-  expect_identical(c(nrow(summary(out, redundant = TRUE)), ncol(summary(out, redundant = TRUE))), c(12L, 6L))
+  expect_identical(
+    c(
+      nrow(summary(out, redundant = TRUE)),
+      ncol(summary(out, redundant = TRUE))
+    ),
+    c(12L, 6L)
+  )
   expect_identical(c(nrow(summary(out)), ncol(summary(out))), c(9L, 5L))
 
   # pipe and select
@@ -142,7 +194,13 @@ test_that("format checks", {
       select2 = c("Sepal.Length", "Sepal.Width")
     )
   expect_identical(c(nrow(out), ncol(out)), c(2L, 11L))
-  expect_identical(c(nrow(summary(out, redundant = TRUE)), ncol(summary(out, redundant = TRUE))), c(1L, 3L))
+  expect_identical(
+    c(
+      nrow(summary(out, redundant = TRUE)),
+      ncol(summary(out, redundant = TRUE))
+    ),
+    c(1L, 3L)
+  )
   expect_identical(c(nrow(summary(out)), ncol(summary(out))), c(1L, 3L))
   expect_equal(out[["r"]], c(0.8179, -0.3661), tolerance = 1e-2)
   expect_identical(out$Parameter1, c("Petal.Width", "Petal.Width"))
@@ -152,7 +210,6 @@ test_that("format checks", {
   skip_if_not_or_load_if_installed("BayesFactor")
   skip_if_not_or_load_if_installed("lme4")
 
-
   out <- correlation(
     iris,
     include_factors = TRUE,
@@ -162,7 +219,13 @@ test_that("format checks", {
     partial_bayesian = TRUE
   )
   expect_identical(c(nrow(out), ncol(out)), c(6L, 14L))
-  expect_identical(c(nrow(summary(out, redundant = TRUE)), ncol(summary(out, redundant = TRUE))), c(4L, 5L))
+  expect_identical(
+    c(
+      nrow(summary(out, redundant = TRUE)),
+      ncol(summary(out, redundant = TRUE))
+    ),
+    c(4L, 5L)
+  )
   expect_identical(c(nrow(summary(out)), ncol(summary(out))), c(3L, 4L))
 })
 
@@ -176,7 +239,10 @@ test_that("specific types", {
     y = as.ordered(sample(letters[1:5], 20, TRUE))
   )
 
-  expect_warning(correlation(data, method = "polychoric"), regex = "It seems like")
+  expect_warning(
+    correlation(data, method = "polychoric"),
+    regex = "It seems like"
+  )
 })
 
 test_that("correlation doesn't fail when BFs are NA", {
@@ -215,24 +281,26 @@ test_that("correlation output with zap_small", {
 test_that("missing values", {
   data <- mtcars
 
-  data[1,1] <- NA
+  data[1, 1] <- NA
 
-  r_pairwise <- stats::cor(data[,1:5], use = "pairwise")
-  r_complete <- stats::cor(data[,1:5], use = "complete")
+  r_pairwise <- stats::cor(data[, 1:5], use = "pairwise")
+  r_complete <- stats::cor(data[, 1:5], use = "complete")
 
-  corr_pairwise <- correlation(data[,1:5])
-  corr_complete <- correlation(data[,1:5], missing = "keep_complete")
+  corr_pairwise <- correlation(data[, 1:5])
+  corr_complete <- correlation(data[, 1:5], missing = "keep_complete")
 
   expect_equal(as.matrix(corr_pairwise), r_pairwise)
   expect_equal(as.matrix(corr_complete), r_complete)
 
+  r_pairwise <- stats::cor(data[, 1:2], data[, 3:5], use = "pairwise")
+  r_complete <- stats::cor(data[, 1:2], data[, 3:5], use = "complete")
 
-
-  r_pairwise <- stats::cor(data[,1:2], data[,3:5], use = "pairwise")
-  r_complete <- stats::cor(data[,1:2], data[,3:5], use = "complete")
-
-  corr_pairwise <- correlation(data[,1:2], data[,3:5])
-  corr_complete <- correlation(data[,1:2], data[,3:5], missing = "keep_complete")
+  corr_pairwise <- correlation(data[, 1:2], data[, 3:5])
+  corr_complete <- correlation(
+    data[, 1:2],
+    data[, 3:5],
+    missing = "keep_complete"
+  )
 
   expect_equal(as.matrix(corr_pairwise), r_pairwise)
   expect_equal(as.matrix(corr_complete), r_complete)
diff --git a/tests/testthat/test-display_print_dataframe.R b/tests/testthat/test-display_print_dataframe.R
index 62465552..f50a0180 100644
--- a/tests/testthat/test-display_print_dataframe.R
+++ b/tests/testthat/test-display_print_dataframe.R
@@ -12,6 +12,15 @@ test_that("display and print method works - HTML", {
   skip_if(getRversion() < "4.0.0")
   skip_if_not_or_load_if_installed("gt")
 
-  expect_s3_class(display(correlation(subset(mtcars, select = c("wt", "mpg"))), format = "html"), "gt_tbl")
-  expect_s3_class(print_html(correlation(subset(mtcars, select = c("wt", "mpg")))), "gt_tbl")
+  expect_s3_class(
+    display(
+      correlation(subset(mtcars, select = c("wt", "mpg"))),
+      format = "html"
+    ),
+    "gt_tbl"
+  )
+  expect_s3_class(
+    print_html(correlation(subset(mtcars, select = c("wt", "mpg")))),
+    "gt_tbl"
+  )
 })
diff --git a/tests/testthat/test-methods.R b/tests/testthat/test-methods.R
index 5b198a0e..12996330 100644
--- a/tests/testthat/test-methods.R
+++ b/tests/testthat/test-methods.R
@@ -9,5 +9,8 @@ test_that("summary.correlation - target column", {
   expect_snapshot(summary(correlation(ggplot2::msleep), target = "t"))
   expect_snapshot(summary(correlation(ggplot2::msleep), target = "df_error"))
   expect_snapshot(summary(correlation(ggplot2::msleep), target = "p"))
-  expect_error(summary(correlation(ggplot2::msleep), target = "not_a_column_name"))
+  expect_error(summary(
+    correlation(ggplot2::msleep),
+    target = "not_a_column_name"
+  ))
 })
diff --git a/tests/testthat/test-renaming.R b/tests/testthat/test-renaming.R
index 6cb7d79e..fc82c8e9 100644
--- a/tests/testthat/test-renaming.R
+++ b/tests/testthat/test-renaming.R
@@ -1,19 +1,18 @@
 test_that("renaming columns", {
   # should warn the user
   expect_warning({
-    out <- correlation(anscombe,
-      select = c("x1", "x2"),
-      rename = "var1"
-    )
+    out <- correlation(anscombe, select = c("x1", "x2"), rename = "var1")
   })
   expect_snapshot(print(out))
 
-  expect_snapshot(correlation(anscombe,
+  expect_snapshot(correlation(
+    anscombe,
     select = c("x1", "x2"),
     rename = c("var1", "var2")
   ))
 
-  expect_snapshot(correlation(anscombe,
+  expect_snapshot(correlation(
+    anscombe,
     select = c("x1", "x2"),
     select2 = c("y1", "y2"),
     rename = c("var1", "var2")