Remarks regarding JOSS paper #34

adress ...

R/acca.R

@@ -17,7 +17,7 @@
 #' @param maxrep \[\code{integer(1)}]\cr maximum number of
 #' interactions without change in the clusters.
 #' @param maxiter \[\code{integer(1)}]\cr maximum number of interactions.
-#' @param ... Additional arguments .
+#' @param ... Not used. Included for S3 method consistency.
 #'
 #' @return \[\code{acca_list(k)}]\cr A list with the
 #' final result of the clustering method.

R/best_acca.R

@@ -12,7 +12,7 @@
 #' without change in the clusters in the ACCA method.
 #' @param maxiter \[\code{integer(1)}]\cr maximum number
 #' of interactions in the ACCA method.
-#' @param ... Additional arguments.
+#' @param ... Not used. Included for S3 method consistency.
 #'
 #' @return \[\code{list(3)}]\cr A list with:
 #' silhouette average with per k `$silhouette.ave`;

R/corr_fun.R

@@ -61,8 +61,7 @@ corr_fun <- function(df,
                      mic.args = list(),
                      pps.args = list(ptest = FALSE),
                      cramersV.args = list(),
-                     uncoef.args = list(),
-                     ...) {
+                     uncoef.args = list()) {
   assert_required_argument(df, "The 'df' argument must be a data.frame containing the data to analyze.")
   assert_required_argument(nx, "The 'nx' argument must be a character vector specifying a column name from 'df' for the independent variable(s).")
   assert_required_argument(ny, "The 'ny' argument must be a character string specifying a column name from 'df' for the dependent variable.")

R/corrp.R

@@ -90,7 +90,6 @@
 #' @param pps.args \[\code{list(1)}]\cr additional parameters for the predictive power score to be passed to \code{\link[ppsr]{score}}.
 #' @param uncoef.args \[\code{list(1)}]\cr additional parameters for the uncertainty coefficient to be passed to \code{\link[DescTools]{UncertCoef}}.
 #' @param cramersV.args \[\code{list(1)}]\cr additional parameters for the Cramer's V to be passed to \code{\link[lsr]{cramersV}}.
-#' @param ... Additional arguments.
 #'
 #' @author Igor D.S. Siciliani, Paulo H. dos Santos
 #'
@@ -124,8 +123,7 @@ corrp <- function(df,
                   mic.args = list(),
                   pps.args = list(ptest = FALSE),
                   cramersV.args = list(),
-                  uncoef.args = list(),
-                  ...) {
+                  uncoef.args = list()) {
   assert_required_argument(df, "The 'df' argument must be a data.frame containing the data to analyze.")
   alternative <- match.arg(alternative)
   cor.nn <- match.arg(cor.nn)
@@ -169,7 +167,7 @@ corrp <- function(df,
     })
     corr <- parallel::clusterApply(
       cluster, seq_len(NROW(index.grid)),
-      function(k, ...) {
+      function(k) {
         ny <- cnames[index.grid[["i"]][k]]
         nx <- cnames[index.grid[["j"]][k]]
         corr_fun(df,
@@ -198,7 +196,7 @@ corrp <- function(df,
     # Sequential corr_fun
     corr <- lapply(
       seq_len(NROW(index.grid)),
-      function(k, ...) {
+      function(k) {
         ny <- cnames[index.grid[["i"]][k]]
         nx <- cnames[index.grid[["j"]][k]]
         corr_fun(df,