Merge branch 'main' into openxlsx2

DESCRIPTION

@@ -1,8 +1,8 @@
 Type: Package
 Package: correlation
 Title: Methods for Correlation Analysis
-Version: 0.8.5
-Authors@R: 
+Version: 0.8.5.1
+Authors@R:
     c(person(given = "Dominique",
              family = "Makowski",
              role = c("aut", "inv"),
@@ -28,19 +28,19 @@ Authors@R:
              role = "aut",
              email = "[email protected]",
              comment = c(ORCID = "0000-0002-4287-4801", Twitter = "@mattansb")),
-      person(given = "Rémi", 
+      person(given = "Rémi",
              family = "Thériault",
-             role = c("aut"), 
+             role = c("aut"),
              email = "[email protected]",
              comment = c(ORCID = "0000-0003-4315-6788", Twitter = "@rempsyc")),
-      person(given = "Mark", 
-             family = "White", 
+      person(given = "Mark",
+             family = "White",
              email = "[email protected]",
              role = "rev"),
-      person(given = "Maximilian M.", 
-             family = "Rabe", 
-             email = "[email protected]", 
-             role = "rev", 
+      person(given = "Maximilian M.",
+             family = "Rabe",
+             email = "[email protected]",
+             role = "rev",
              comment = c(ORCID = "0000-0002-2556-5644")))
 Maintainer: Brenton M. Wiernik <[email protected]>
 Description: Lightweight package for computing different kinds
@@ -56,7 +56,7 @@ Depends:
 Imports:
     bayestestR (>= 0.13.2),
     datasets,
-    datawizard (>= 0.11.0),
+    datawizard (>= 0.10.0),
     insight (>= 0.20.0),
     parameters (>= 0.21.7),
     stats
@@ -84,11 +84,11 @@ Suggests:
     wdm,
     WRS2,
     openxlsx2 (>= 1.0)
-VignetteBuilder: 
+VignetteBuilder:
     knitr
 Encoding: UTF-8
 Language: en-US
-RoxygenNote: 7.3.1
+RoxygenNote: 7.3.2
 Roxygen: list(markdown = TRUE)
 Config/testthat/edition: 3
 Config/Needs/website:

NEWS.md

@@ -1,3 +1,7 @@
+# correlation 0.8.6
+
+- Fix CRAN check issues.
+
 # correlation 0.8.5
 
 - New `cormatrix_to_excel()` function for exporting correlation matrices to Excel with color formatting.
@@ -206,4 +210,3 @@
 - Fixed issue in `correlation()` for correlation coefficients with less than
   four complete pairs of observations (in such cases, `cor_test()` now returns
   `NA` for the confidence intervals).
-

R/cor_test.R

@@ -8,18 +8,17 @@
 #' @param ci Confidence/Credible Interval level. If `"default"`, then it is
 #'   set to `0.95` (`95%` CI).
 #' @param method A character string indicating which correlation coefficient is
-#'   to be used for the test. One of `"pearson"` (default),
-#'   `"kendall"`, `"spearman"` (but see also the `robust` argument), `"biserial"`,
-#'   `"polychoric"`, `"tetrachoric"`, `"biweight"`,
-#'   `"distance"`, `"percentage"` (for percentage bend correlation),
-#'   `"blomqvist"` (for Blomqvist's coefficient), `"hoeffding"` (for
-#'   Hoeffding's D), `"gamma"`, `"gaussian"` (for Gaussian Rank
-#'   correlation) or `"shepherd"` (for Shepherd's Pi correlation). Setting
-#'   `"auto"` will attempt at selecting the most relevant method
-#'   (polychoric when ordinal factors involved, tetrachoric when dichotomous
-#'   factors involved, point-biserial if one dichotomous and one continuous and
-#'   pearson otherwise). See below the **details** section for a description of
-#'   these indices.
+#'   to be used for the test. One of `"pearson"` (default), `"kendall"`,
+#'   `"spearman"` (but see also the `robust` argument), `"biserial"`,
+#'   `"polychoric"`, `"tetrachoric"`, `"biweight"`, `"distance"`, `"percentage"`
+#'   (for percentage bend correlation), `"blomqvist"` (for Blomqvist's
+#'   coefficient), `"hoeffding"` (for Hoeffding's D), `"gamma"`, `"gaussian"`
+#'   (for Gaussian Rank correlation) or `"shepherd"` (for Shepherd's Pi
+#'   correlation). Setting `"auto"` will attempt at selecting the most relevant
+#'   method (polychoric when ordinal factors involved, tetrachoric when
+#'   dichotomous factors involved, point-biserial if one dichotomous and one
+#'   continuous and pearson otherwise). See below the **details** section for a
+#'   description of these indices.
 #' @param bayesian If `TRUE`, will run the correlations under a Bayesian
 #'   framework.
 #' @param partial_bayesian If partial correlations under a Bayesian framework
@@ -28,30 +27,29 @@
 #'   pseudo-Bayesian partial correlations (i.e., Bayesian correlation based on
 #'   frequentist partialization).
 #' @param include_factors If `TRUE`, the factors are kept and eventually
-#'   converted to numeric or used as random effects (depending of
-#'   `multilevel`). If `FALSE`, factors are removed upfront.
-#' @param partial Can be `TRUE` or `"semi"` for partial and
-#'   semi-partial correlations, respectively.
+#'   converted to numeric or used as random effects (depending of `multilevel`).
+#'   If `FALSE`, factors are removed upfront.
+#' @param partial Can be `TRUE` or `"semi"` for partial and semi-partial
+#'   correlations, respectively.
 #' @inheritParams datawizard::adjust
 #' @param bayesian_prior For the prior argument, several named values are
-#'   recognized: `"medium.narrow"`, `"medium"`, `"wide"`, and
-#'   `"ultrawide"`. These correspond to scale values of `1/sqrt(27)`,
-#'   `1/3`, `1/sqrt(3)` and `1`, respectively. See the
-#'   `BayesFactor::correlationBF` function.
+#'   recognized: `"medium.narrow"`, `"medium"`, `"wide"`, and `"ultrawide"`.
+#'   These correspond to scale values of `1/sqrt(27)`, `1/3`, `1/sqrt(3)` and
+#'   `1`, respectively. See the `BayesFactor::correlationBF` function.
 #' @param bayesian_ci_method,bayesian_test See arguments in
-#'   [`model_parameters()`][parameters] for `BayesFactor` tests.
+#'   [`parameters::model_parameters()`] for `BayesFactor` tests.
 #' @param ranktransform If `TRUE`, will rank-transform the variables prior to
 #'   estimating the correlation, which is one way of making the analysis more
-#'   resistant to extreme values (outliers). Note that, for instance, a Pearson's
-#'   correlation on rank-transformed data is equivalent to a Spearman's rank
-#'   correlation. Thus, using `robust=TRUE` and `method="spearman"` is
-#'   redundant. Nonetheless, it is an easy option to increase the robustness of the
-#'   correlation as well as flexible way to obtain Bayesian or multilevel
-#'   Spearman-like rank correlations.
+#'   resistant to extreme values (outliers). Note that, for instance, a
+#'   Pearson's correlation on rank-transformed data is equivalent to a
+#'   Spearman's rank correlation. Thus, using `robust=TRUE` and
+#'   `method="spearman"` is redundant. Nonetheless, it is an easy option to
+#'   increase the robustness of the correlation as well as flexible way to
+#'   obtain Bayesian or multilevel Spearman-like rank correlations.
 #' @param winsorize Another way of making the correlation more "robust" (i.e.,
-#'   limiting the impact of extreme values). Can be either `FALSE` or a
-#'   number between 0 and 1 (e.g., `0.2`) that corresponds to the desired
-#'   threshold. See the [`winsorize()`][winsorize] function for more details.
+#'   limiting the impact of extreme values). Can be either `FALSE` or a number
+#'   between 0 and 1 (e.g., `0.2`) that corresponds to the desired threshold.
+#'   See the [`datawizard::winsorize()`] function for more details.
 #' @param verbose Toggle warnings.
 #' @param ... Additional arguments (e.g., `alternative`) to be passed to
 #'   other methods. See `stats::cor.test` for further details.

R/cor_test_bayes.R

@@ -58,7 +58,11 @@
 
   if (x == y) {
     # Avoid error in the case of perfect correlation
-    rez <- BayesFactor::correlationBF(stats::rnorm(1000), stats::rnorm(1000), rscale = bayesian_prior)
+    rez <- suppressWarnings(BayesFactor::correlationBF(
+      stats::rnorm(1000),
+      stats::rnorm(1000),
+      rscale = bayesian_prior
+    ))
     params <- parameters::model_parameters(
       rez,
       dispersion = FALSE,
@@ -79,7 +83,11 @@
     if ("ROPE_Percentage" %in% names(params)) params$ROPE_Percentage <- 0
     if ("BF" %in% names(params)) params$BF <- Inf
   } else {
-    rez <- BayesFactor::correlationBF(var_x, var_y, rscale = bayesian_prior)
+    rez <- suppressWarnings(BayesFactor::correlationBF(
+      var_x,
+      var_y,
+      rscale = bayesian_prior
+    ))
     params <- parameters::model_parameters(
       rez,
       dispersion = FALSE,