General comments for review #27

corr_fun

R/corr_fun.R

@@ -53,9 +53,8 @@
 #'
 #'
 #' @param df \[\code{data.frame(1)}]\cr input data frame.
-#' @param nx \[\code{character(1)}]\cr column name of 
-#' independent/predictor variable.
-#' @param ny \[\code{character(1)}]\cr column name of dependent/target variable.
+#' @param nx \[\code{character(1)}]\cr first variable column name: independent/predictor variable. 
+#' @param ny \[\code{character(1)}]\cr second variable column name: dependent/target variable.
 #' @param p.value \[\code{logical(1)}]\cr
 #' P-value probability of obtaining the observed results of a test,
 #' assuming that the null hypothesis is correct. By default p.value=0.05 (Cutoff value for p-value.).
@@ -87,7 +86,7 @@
 #' @param pps.args \[\code{list(1)}]\cr additional parameters for the specific method.
 #' @param uncoef.args \[\code{list(1)}]\cr additional parameters for the specific method.
 #' @param cramersV.args \[\code{list(1)}]\cr additional parameters for the specific method.
-#' @param ... Additional arguments (TODO).
+#' @param ... Additional arguments.
 #'
 #'
 #' @author Igor D.S. Siciliani
@@ -245,7 +244,7 @@ corr_fun <- function(df,
   }
 
   if ((class(r) %in% "try-error")) {
-    msg <- ""
+    msg <- "" #
 
     if (verbose) {
       warnings(cat(

README.md

@@ -21,7 +21,7 @@ The data.frame is allowed to have columns of these four classes: integer, numeri
 In this new package the correlation is automatically computed according to the follow options: 
 
 #### integer/numeric pair:
-- [Pearson correlation test](https://en.wikipedia.org/wiki/Pearson_correlation_coefficient) ;
+- [Pearson correlation test](https://en.wikipedia.org/wiki/Pearson_correlation_coefficient);
 - [Distance Correlation](https://en.wikipedia.org/wiki/Distance_correlation);
 - [Maximal Information Coefficient](https://en.wikipedia.org/wiki/Maximal_information_coefficient);
 - [Predictive Power Score](https://github.com/paulvanderlaken/ppsr).
@@ -66,7 +66,7 @@ remotes::install_github("meantrix/corrp@main")
 
 ```r
 # coorp with using iris using parallel processing
-results = corrp::corrp(iris, cor.nn = 'mic', cor.nc = 'pps',cor.cc = 'uncoef', n.cores = 2 , verbose = FALSE)
+results = corrp::corrp(iris, cor.nn = 'pps', cor.nc = 'pps',cor.cc = 'pps', n.cores = 2 , verbose = FALSE)
 # an sequential example with different correlation pair types
 results_2 = corrp::corrp(mtcars, cor.nn = 'pps', cor.nc = 'lm', cor.cc = 'cramersV', verbose = FALSE)