diff --git a/DESCRIPTION b/DESCRIPTION
index 34185bf..e8c3a93 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -19,6 +19,7 @@ Description: Compute multiple types of correlation analyses, including
 Imports: 
     Rcpp (>= 1.0.13-1),
     RcppArmadillo (>= 14.2.2-1),
+    corrplot (>= 0.95),
     lsr (>= 0.5.2),
     parallel (>= 4.4.1),
     stats (>= 4.4.1),
@@ -28,7 +29,6 @@ Imports:
     ppsr (>= 0.0.2),
     DescTools (>= 0.99.40)
 Suggests: 
-    corrplot,
     energy,
     knitr,
     rmarkdown,
diff --git a/NAMESPACE b/NAMESPACE
index b995b61..2edd4ae 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -21,6 +21,7 @@ export(corr_rm)
 export(corrp)
 export(dcorT_test)
 export(ptest)
+export(set_arguments)
 export(sil_acca)
 importFrom(Rcpp,evalCpp)
 importFrom(RcppArmadillo,armadillo_version)
diff --git a/R/acca.R b/R/acca.R
index 2e206f1..79cdee7 100644
--- a/R/acca.R
+++ b/R/acca.R
@@ -23,7 +23,7 @@
 #' final result of the clustering method.
 #'  That is, the name of the variables belonging to each cluster k.
 #'
-#' @author Igor D.S. Siciliani
+#' @author Igor D.S. Siciliani, Paulo H. dos Santos
 #'
 #' @keywords correlation , acca
 #'
diff --git a/R/best_acca.R b/R/best_acca.R
index adc6b4c..5f3415b 100644
--- a/R/best_acca.R
+++ b/R/best_acca.R
@@ -20,7 +20,7 @@
 #' the optimal number of clusters `$best.k`.
 #' @seealso \code{\link{sil_acca}}
 #'
-#' @author Igor D.S. Siciliani
+#' @author Igor D.S. Siciliani, Paulo H. dos Santos
 #'
 #' @keywords silhouette , acca , optimal , k
 #'
diff --git a/R/corr_fun.R b/R/corr_fun.R
index e3047d3..6a22b4a 100644
--- a/R/corr_fun.R
+++ b/R/corr_fun.R
@@ -8,38 +8,8 @@
 #'
 #' @name corr_fun
 #'
-#' @section Details (Types):
+#' @inheritSection corrp Pair Types
 #'
-#' - \code{integer/numeric pair} Pearson Correlation using
-#' \code{\link[stats]{cor}} function. The
-#'   value lies between -1 and 1.\cr
-#' - \code{integer/numeric pair} Distance Correlation
-#' using \code{\link[energy]{dcorT.test}} function. The
-#'   value lies between 0 and 1.\cr
-#' - \code{integer/numeric pair} Maximal Information Coefficient using
-#' \code{\link[minerva]{mine}} function. The
-#'   value lies between 0 and 1.\cr
-#' - \code{integer/numeric pair} Predictive Power Score using 
-#' \code{\link[ppsr]{score}} function. The
-#'   value lies between 0 and 1.\cr\cr
-#' - \code{integer/numeric - factor/categorical pair} correlation coefficient or
-#'   squared root of R^2 coefficient of linear regression of integer/numeric
-#'   variable over factor/categorical variable using 
-#' \code{\link[stats]{lm}} function. The value
-#'   lies between 0 and 1.\cr
-#' - \code{integer/numeric - factor/categorical pair} 
-#' Predictive Power Score using \code{\link[ppsr]{score}} function. 
-#' The value lies between 0 and 1.\cr\cr
-#' - \code{factor/categorical pair} Cramer's V value is
-#'   computed based on chisq test and using 
-#' \code{\link[lsr]{cramersV}} function. The value lies
-#'   between 0 and 1.\cr
-#' - \code{factor/categorical pair} Uncertainty coefficient 
-#' using \code{\link[DescTools]{UncertCoef}} function. The
-#'   value lies between 0 and 1.\cr
-#' - \code{factor/categorical pair} Predictive Power Score 
-#' using \code{\link[ppsr]{score}} function. 
-#' The value lies between 0 and 1.\cr
 #'
 #' @return list with all statistical results.\cr
 #' - All statistical tests are controlled by the confidence internal of
@@ -52,44 +22,12 @@
 #' default the association measure(`infer.value`) will be `NA`.
 #'
 #'
-#' @param df \[\code{data.frame(1)}]\cr input data frame.
+#' @inheritParams corrp
 #' @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.).
-#' @param comp \[\code{character(1)}]\cr The param \code{p.value} must be greater
-#'  or less than those estimated in tests and correlations.
-#' @param alternative \[\code{character(1)}]\cr a character string specifying the alternative hypothesis,
-#' must be one of "greater" (default), "less" or "two.sided". You can specify just the initial letter.
-#' You can specify just the initial letter.
-#' @param verbose \[\code{logical(1)}]\cr Activate verbose mode.
-#' @param num.s \[\code{numeric(1)}]\cr Used in permutation test. The number of samples with
-#' replacement created with y numeric vector.
-#' @param rk \[\code{logical(1)}]\cr Used in permutation test.
-#' if its TRUE transform x, y numeric vectors with samples ranks.
-#' @param cor.nn \[\code{character(1)}]\cr
-#' Choose correlation type to be used in integer/numeric pair inference.
-#' The options are `pearson: Pearson Correlation`,`mic: Maximal Information Coefficient`,
-#' `dcor: Distance Correlation`,`pps: Predictive Power Score`.Default is `Pearson Correlation`.
-#' @param cor.nc \[\code{character(1)}]\cr
-#' Choose correlation type to be used in integer/numeric - factor/categorical pair inference.
-#' The option are `lm: Linear Model`,`pps: Predictive Power Score`. Default is `Linear Model`.
-#' @param cor.cc  \[\code{character(1)}]\cr
-#' Choose correlation type to be used in factor/categorical pair inference.
-#' The option are `cramersV: Cramer's V`,`uncoef: Uncertainty coefficient`,
-#' `pps: Predictive Power Score`. Default is ` Cramer's V`.
-#' @param lm.args \[\code{list(1)}]\cr additional parameters for the specific method.
-#' @param pearson.args \[\code{list(1)}]\cr additional parameters for the specific method.
-#' @param dcor.args \[\code{list(1)}]\cr additional parameters for the specific method.
-#' @param mic.args \[\code{list(1)}]\cr additional parameters for the specific method.
-#' @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.
 #'
 #'
-#' @author Igor D.S. Siciliani
+#' @author Igor D.S. Siciliani, Paulo H. dos Santos
 #'
 #' @keywords correlation , power predictive score , linear model , distance correlation ,
 #' mic , point biserial , pearson , cramer'sV
diff --git a/R/corr_matrix.R b/R/corr_matrix.R
index e0e9332..4a7695f 100644
--- a/R/corr_matrix.R
+++ b/R/corr_matrix.R
@@ -9,7 +9,7 @@
 #' be represented by NA or FALSE in the correlation matrix.
 #' @param ... Additional arguments (TODO).
 #'
-#' @author Igor D.S. Siciliani
+#' @author Igor D.S. Siciliani, Paulo H. dos Santos
 #'
 #' @keywords correlation matrix , corrp
 #'
@@ -17,7 +17,7 @@
 #'
 #' iris_cor <- corrp(iris)
 #' iris_m <- corr_matrix(iris_cor, isig = FALSE)
-#' corrplot(iris_m)
+#' corrplot::corrplot(iris_m)
 #' @export
 corr_matrix <- function(c, ...) {
   assert_required_argument(c, "The 'c' argument must be a clist object, which is the output from corrp.")
diff --git a/R/corr_rm.R b/R/corr_rm.R
index 18ddd9e..c810abd 100644
--- a/R/corr_rm.R
+++ b/R/corr_rm.R
@@ -6,8 +6,8 @@
 #'
 #' @param df \[\code{data.frame(1)}]\cr input data frame.
 #' @param c \[\code{clist(1)} | \code{cmatrix(1)}]\cr correlation list output from the function \code{\link[corrp]{corrp}} 
-#' with class \code{\link[corrp]{clist}} or correlation matrix output 
-#' from \code{\link[corrp]{corr_matrix}} with class \code{\link[corrp]{cmatrix}}.
+#' with class \code{clist} or correlation matrix output 
+#' from \code{\link[corrp]{corr_matrix}} with class \code{cmatrix}.
 #' @param cutoff \[\code{numeric(1)}]\cr A numeric value for the pair-wise absolute correlation cutoff.
 #' The default values is 0.75.
 #' @param col \[\code{character(1)}]\cr choose the column to be used in the correlation matrix
@@ -22,7 +22,7 @@
 #' corr_rm(df = iris, c = iris_clist, cutoff = 0.75, col = "infer.value", isig = FALSE)
 #' corr_rm(df = iris, c = iris_cmatrix, cutoff = 0.75, col = "infer.value", isig = FALSE)
 #'
-#' @author Igor D.S. Siciliani
+#' @author Igor D.S. Siciliani, Paulo H. dos Santos
 #'
 #' @keywords highly correlated , cmatrix , clist
 #'
diff --git a/R/corrp.R b/R/corrp.R
index d2d5d1a..6562da4 100644
--- a/R/corrp.R
+++ b/R/corrp.R
@@ -8,29 +8,25 @@
 #'
 #' @name corrp
 #'
-#' @section Details (Pair Types):
+#' @section Pair Types:
 #'
-#' - \code{integer/numeric pair} Pearson Correlation using \code{\link[stats]{cor}} function. The
-#'   value lies between -1 and 1.\cr
-#' - \code{integer/numeric pair} Distance Correlation using \code{\link[energy]{dcorT.test}} function. The
-#'   value lies between 0 and 1.\cr
-#' - \code{integer/numeric pair} Maximal Information Coefficient using \code{\link[minerva]{mine}} function. The
-#'   value lies between 0 and 1.\cr
-#' - \code{integer/numeric pair} Predictive Power Score using \code{\link[ppsr]{score}} function. The
-#'   value lies between 0 and 1.\cr\cr
-#' - \code{integer/numeric - factor/categorical pair} correlation coefficient or
-#'   squared root of R^2 coefficient of linear regression of integer/numeric
-#'   variable over factor/categorical variable using \code{\link[stats]{lm}} function. The value
-#'   lies between 0 and 1.\cr
-#' - \code{integer/numeric - factor/categorical pair} Predictive Power Score using \code{\link[ppsr]{score}} function. The
-#'   value lies between 0 and 1.\cr\cr
-#' - \code{factor/categorical pair} Cramer's V value is
-#'   computed based on chisq test and using \code{\link[lsr]{cramersV}} function. The value lies
-#'   between 0 and 1.\cr
-#' - \code{factor/categorical pair} Uncertainty coefficient using \code{\link[DescTools]{UncertCoef}} function. The
-#'   value lies between 0 and 1.\cr
-#' - \code{factor/categorical pair} Predictive Power Score using \code{\link[ppsr]{score}} function. The
-#'   value lies between 0 and 1.\cr
+#' **Numeric pairs (integer/numeric):**
+#' 
+#' - **Pearson Correlation Coefficient:** A widely used measure of the strength and direction of linear relationships. Implemented using \code{\link[stats]{cor}}. For more details, see \url{https://doi.org/10.1098/rspl.1895.0041}. The value lies between -1 and 1.\cr
+#' - **Distance Correlation:** Based on the idea of expanding covariance to distances, it measures both linear and nonlinear associations between variables. Implemented using \code{\link[energy]{dcorT.test}}. For more details, see \url{https://doi.org/10.1214/009053607000000505}. The value lies between 0 and 1.\cr
+#' - **Maximal Information Coefficient (MIC):** An information-based nonparametric method that can detect both linear and non-linear relationships between variables. Implemented using \code{\link[minerva]{mine}}. For more details, see \url{https://doi.org/10.1126/science.1205438}. The value lies between 0 and 1.\cr
+#' - **Predictive Power Score (PPS):** A metric used to assess predictive relations between variables. Implemented using \code{\link[ppsr]{score}}. For more details, see \url{https://zenodo.org/record/4091345}. The value lies between 0 and 1.\cr\cr
+#'
+#' **Numeric and categorical pairs (integer/numeric - factor/categorical):**
+#' 
+#' - **Square Root of R² Coefficient:** From linear regression of the numeric variable over the categorical variable. Implemented using \code{\link[stats]{lm}}. For more details, see \url{https://doi.org/10.4324/9780203774441}. The value lies between 0 and 1.\cr
+#' - **Predictive Power Score (PPS):** A metric used to assess predictive relations between numeric and categorical variables. Implemented using \code{\link[ppsr]{score}}. For more details, see \url{https://zenodo.org/record/4091345}. The value lies between 0 and 1.\cr\cr
+#'
+#' **Categorical pairs (factor/categorical):**
+#' 
+#' - **Cramér's V:** A measure of association between nominal variables. Computed based on a chi-squared test and implemented using \code{\link[lsr]{cramersV}}. For more details, see \url{https://doi.org/10.1515/9781400883868}. The value lies between 0 and 1.\cr
+#' - **Uncertainty Coefficient:** A measure of nominal association between two variables. Implemented using \code{\link[DescTools]{UncertCoef}}. For more details, see \url{https://doi.org/10.1016/j.jbi.2010.02.001}. The value lies between 0 and 1.\cr
+#' - **Predictive Power Score (PPS):** A metric used to assess predictive relations between categorical variables. Implemented using \code{\link[ppsr]{score}}. For more details, see \url{https://zenodo.org/record/4091345}. The value lies between 0 and 1.\cr
 #'
 #' @return 
 #' A list with two tables: `data` and `index`.
@@ -54,9 +50,11 @@
 #' All statistical tests are controlled by the confidence internal of
 #'   p.value param. If the statistical tests do not obtain a significance greater/less
 #'   than p.value the value of variable `isig` will be `FALSE`.\cr
-#' By default there is no statistical significance test for the pps algorithm. By default `isig` is NA, you can enable in the pps.args.\cr
+
 #' If any errors occur during operations the association measure (`infer.value`) will be `NA`.\cr
 #' The result `data` and `index` will have \eqn{N^2} rows, where N is the number of variables of the input data.
+#' By default there is no statistical significance test for the pps algorithm. By default `isig` is NA, you can enable in the `pps.args` setting `ptest = TRUE`.\cr
+#' All the `*.args` can modified the parameters (`p.value`, `comp`, `alternative`, `num.s`, `rk`, `ptest`) for the respective method on it's prefix.
 #'
 #' @param df \[\code{data.frame(1)}]\cr input data frame.
 #' @param parallel \[\code{logical(1)}]\cr If its TRUE run the operations in parallel backend.
@@ -85,16 +83,16 @@
 #' Choose correlation type to be used in factor/categorical pair inference.
 #' The option are `cramersV: Cramer's V`,`uncoef: Uncertainty coefficient`,
 #' `pps: Predictive Power Score`. Default is ` Cramer's V`.
-#' @param lm.args \[\code{list(1)}]\cr additional parameters for the specific method.
-#' @param pearson.args \[\code{list(1)}]\cr additional parameters for the specific method.
-#' @param dcor.args \[\code{list(1)}]\cr additional parameters for the specific method.
-#' @param mic.args \[\code{list(1)}]\cr additional parameters for the specific method.
-#' @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 lm.args \[\code{list(1)}]\cr additional parameters for linear model to be passed to \code{\link[stats]{lm}}. 
+#' @param pearson.args \[\code{list(1)}]\cr additional parameters for Pearson correlation to be passed to \code{\link[stats]{cor.test}}.
+#' @param dcor.args \[\code{list(1)}]\cr additional parameters for the distance correlation to be passed to \code{\link[corrp]{dcorT_test}}. 
+#' @param mic.args \[\code{list(1)}]\cr additional parameters for the maximal information coefficient to be passed to \code{\link[minerva]{mine}}.
+#' @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
+#' @author Igor D.S. Siciliani, Paulo H. dos Santos
 #'
 #' @keywords correlation , power predictive score , linear model , distance correlation ,
 #' mic , point biserial , pearson , cramer'sV
@@ -110,7 +108,7 @@
 #' @examples
 #'  iris_c <- corrp(iris)
 #'  iris_m <- corr_matrix(iris_c, isig = FALSE)
-#'  corrplot(iris_m)
+#'  corrplot::corrplot(iris_m)
 #' 
 #'
 #' @export
diff --git a/R/sil_acca.R b/R/sil_acca.R
index 395b470..bd3e9c4 100644
--- a/R/sil_acca.R
+++ b/R/sil_acca.R
@@ -14,7 +14,7 @@
 #'  are very well clustered.
 #'
 #'
-#' @author Igor D.S. Siciliani
+#' @author Igor D.S. Siciliani, Paulo H. dos Santos
 #'
 #' @keywords silhouette , acca
 #'
diff --git a/R/utils.R b/R/utils.R
index 2434a6f..779dcfc 100644
--- a/R/utils.R
+++ b/R/utils.R
@@ -457,7 +457,7 @@ set_arguments = function(args_list) {
   list_name <- deparse(substitute(args_list))
   
   for (name_arg in names(args_list)) {
-    if (name_arg %in% c("p.value", "comp", "verbose", "alternative", "num.s", "rk", "ptest")) {
+    if (name_arg %in% c("p.value", "comp", "alternative", "num.s", "rk", "ptest")) {
       assign(name_arg, args_list[[name_arg]], envir = parent.frame())
       args_list[[name_arg]] = NULL
     }
diff --git a/man/acca.Rd b/man/acca.Rd
index 7aedb2c..0695aca 100644
--- a/man/acca.Rd
+++ b/man/acca.Rd
@@ -51,7 +51,7 @@ genes with similar pattern of variation in their expression values."
 Journal of Biomedical Informatics 43.4 (2010): 560-568.
 }
 \author{
-Igor D.S. Siciliani
+Igor D.S. Siciliani, Paulo H. dos Santos
 }
 \keyword{,}
 \keyword{acca}
diff --git a/man/best_acca.Rd b/man/best_acca.Rd
index 97df1c4..899b684 100644
--- a/man/best_acca.Rd
+++ b/man/best_acca.Rd
@@ -61,7 +61,7 @@ and Soft Computing. Springer, Cham, 2015.
 \code{\link{sil_acca}}
 }
 \author{
-Igor D.S. Siciliani
+Igor D.S. Siciliani, Paulo H. dos Santos
 }
 \keyword{,}
 \keyword{acca}
diff --git a/man/corr_fun.Rd b/man/corr_fun.Rd
index 244a93d..afed51f 100644
--- a/man/corr_fun.Rd
+++ b/man/corr_fun.Rd
@@ -21,7 +21,7 @@ corr_fun(
   pearson.args = list(),
   dcor.args = list(),
   mic.args = list(),
-  pps.args = list(),
+  pps.args = list(ptest = FALSE),
   cramersV.args = list(),
   uncoef.args = list(),
   ...
@@ -49,8 +49,8 @@ if its TRUE transform x, y numeric vectors with samples ranks.}
 \item{comp}{[\code{character(1)}]\cr The param \code{p.value} must be greater
 or less than those estimated in tests and correlations.}
 
-\item{alternative}{[\code{character(1)}]\cr a character string specifying the alternative hypothesis,
-must be one of "greater" (default), "less" or "two.sided". You can specify just the initial letter.
+\item{alternative}{[\code{character(1)}]\cr a character string specifying the alternative hypothesis for
+the correlation inference. It must be one of "two.sided" (default), "greater" or "less".
 You can specify just the initial letter.}
 
 \item{cor.nn}{[\code{character(1)}]\cr
@@ -67,19 +67,19 @@ Choose correlation type to be used in factor/categorical pair inference.
 The option are \verb{cramersV: Cramer's V},\verb{uncoef: Uncertainty coefficient},
 \verb{pps: Predictive Power Score}. Default is \verb{ Cramer's V}.}
 
-\item{lm.args}{[\code{list(1)}]\cr additional parameters for the specific method.}
+\item{lm.args}{[\code{list(1)}]\cr additional parameters for linear model to be passed to \code{\link[stats]{lm}}.}
 
-\item{pearson.args}{[\code{list(1)}]\cr additional parameters for the specific method.}
+\item{pearson.args}{[\code{list(1)}]\cr additional parameters for Pearson correlation to be passed to \code{\link[stats]{cor.test}}.}
 
-\item{dcor.args}{[\code{list(1)}]\cr additional parameters for the specific method.}
+\item{dcor.args}{[\code{list(1)}]\cr additional parameters for the distance correlation to be passed to \code{\link[corrp]{dcorT_test}}.}
 
-\item{mic.args}{[\code{list(1)}]\cr additional parameters for the specific method.}
+\item{mic.args}{[\code{list(1)}]\cr additional parameters for the maximal information coefficient to be passed to \code{\link[minerva]{mine}}.}
 
-\item{pps.args}{[\code{list(1)}]\cr additional parameters for the specific method.}
+\item{pps.args}{[\code{list(1)}]\cr additional parameters for the predictive power score to be passed to \code{\link[ppsr]{score}}.}
 
-\item{cramersV.args}{[\code{list(1)}]\cr additional parameters for the specific method.}
+\item{cramersV.args}{[\code{list(1)}]\cr additional parameters for the Cramer's V to be passed to \code{\link[lsr]{cramersV}}.}
 
-\item{uncoef.args}{[\code{list(1)}]\cr additional parameters for the specific method.}
+\item{uncoef.args}{[\code{list(1)}]\cr additional parameters for the uncertainty coefficient to be passed to \code{\link[DescTools]{UncertCoef}}.}
 
 \item{...}{Additional arguments.}
 }
@@ -103,39 +103,28 @@ The dataframe is allowed to have columns of these four classes: integer,
 numeric, factor and character. The character column is considered as
 categorical variable.
 }
-\section{Details (Types)}{
+\section{Pair Types}{
 
+
+\strong{Numeric pairs (integer/numeric):}
+\itemize{
+\item \strong{Pearson Correlation Coefficient:} A widely used measure of the strength and direction of linear relationships. Implemented using \code{\link[stats]{cor}}. For more details, see \url{https://doi.org/10.1098/rspl.1895.0041}. The value lies between -1 and 1.\cr
+\item \strong{Distance Correlation:} Based on the idea of expanding covariance to distances, it measures both linear and nonlinear associations between variables. Implemented using \code{\link[energy]{dcorT.test}}. For more details, see \url{https://doi.org/10.1214/009053607000000505}. The value lies between 0 and 1.\cr
+\item \strong{Maximal Information Coefficient (MIC):} An information-based nonparametric method that can detect both linear and non-linear relationships between variables. Implemented using \code{\link[minerva]{mine}}. For more details, see \url{https://doi.org/10.1126/science.1205438}. The value lies between 0 and 1.\cr
+\item \strong{Predictive Power Score (PPS):} A metric used to assess predictive relations between variables. Implemented using \code{\link[ppsr]{score}}. For more details, see \url{https://zenodo.org/record/4091345}. The value lies between 0 and 1.\cr\cr
+}
+
+\strong{Numeric and categorical pairs (integer/numeric - factor/categorical):}
+\itemize{
+\item \strong{Square Root of R² Coefficient:} From linear regression of the numeric variable over the categorical variable. Implemented using \code{\link[stats]{lm}}. For more details, see \url{https://doi.org/10.4324/9780203774441}. The value lies between 0 and 1.\cr
+\item \strong{Predictive Power Score (PPS):} A metric used to assess predictive relations between numeric and categorical variables. Implemented using \code{\link[ppsr]{score}}. For more details, see \url{https://zenodo.org/record/4091345}. The value lies between 0 and 1.\cr\cr
+}
+
+\strong{Categorical pairs (factor/categorical):}
 \itemize{
-\item \code{integer/numeric pair} Pearson Correlation using
-\code{\link[stats]{cor}} function. The
-value lies between -1 and 1.\cr
-\item \code{integer/numeric pair} Distance Correlation
-using \code{\link[energy]{dcorT.test}} function. The
-value lies between 0 and 1.\cr
-\item \code{integer/numeric pair} Maximal Information Coefficient using
-\code{\link[minerva]{mine}} function. The
-value lies between 0 and 1.\cr
-\item \code{integer/numeric pair} Predictive Power Score using
-\code{\link[ppsr]{score}} function. The
-value lies between 0 and 1.\cr\cr
-\item \code{integer/numeric - factor/categorical pair} correlation coefficient or
-squared root of R^2 coefficient of linear regression of integer/numeric
-variable over factor/categorical variable using
-\code{\link[stats]{lm}} function. The value
-lies between 0 and 1.\cr
-\item \code{integer/numeric - factor/categorical pair}
-Predictive Power Score using \code{\link[ppsr]{score}} function.
-The value lies between 0 and 1.\cr\cr
-\item \code{factor/categorical pair} Cramer's V value is
-computed based on chisq test and using
-\code{\link[lsr]{cramersV}} function. The value lies
-between 0 and 1.\cr
-\item \code{factor/categorical pair} Uncertainty coefficient
-using \code{\link[DescTools]{UncertCoef}} function. The
-value lies between 0 and 1.\cr
-\item \code{factor/categorical pair} Predictive Power Score
-using \code{\link[ppsr]{score}} function.
-The value lies between 0 and 1.\cr
+\item \strong{Cramér's V:} A measure of association between nominal variables. Computed based on a chi-squared test and implemented using \code{\link[lsr]{cramersV}}. For more details, see \url{https://doi.org/10.1515/9781400883868}. The value lies between 0 and 1.\cr
+\item \strong{Uncertainty Coefficient:} A measure of nominal association between two variables. Implemented using \code{\link[DescTools]{UncertCoef}}. For more details, see \url{https://doi.org/10.1016/j.jbi.2010.02.001}. The value lies between 0 and 1.\cr
+\item \strong{Predictive Power Score (PPS):} A metric used to assess predictive relations between categorical variables. Implemented using \code{\link[ppsr]{score}}. For more details, see \url{https://zenodo.org/record/4091345}. The value lies between 0 and 1.\cr
 }
 }
 
@@ -153,7 +142,7 @@ Paul van der Laken, ppsr,2021.
 URL \url{https://github.com/paulvanderlaken/ppsr}.
 }
 \author{
-Igor D.S. Siciliani
+Igor D.S. Siciliani, Paulo H. dos Santos
 }
 \keyword{,}
 \keyword{biserial}
diff --git a/man/corr_matrix.Rd b/man/corr_matrix.Rd
index 725b9d0..5104dc1 100644
--- a/man/corr_matrix.Rd
+++ b/man/corr_matrix.Rd
@@ -30,10 +30,10 @@ create a correlation matrix.
 
 iris_cor <- corrp(iris)
 iris_m <- corr_matrix(iris_cor, isig = FALSE)
-corrplot(iris_m)
+corrplot::corrplot(iris_m)
 }
 \author{
-Igor D.S. Siciliani
+Igor D.S. Siciliani, Paulo H. dos Santos
 }
 \keyword{,}
 \keyword{correlation}
diff --git a/man/corr_rm.Rd b/man/corr_rm.Rd
index cc0bec3..5471b42 100644
--- a/man/corr_rm.Rd
+++ b/man/corr_rm.Rd
@@ -36,8 +36,8 @@ corr_rm(df, c, ...)
 \item{df}{[\code{data.frame(1)}]\cr input data frame.}
 
 \item{c}{[\code{clist(1)} | \code{cmatrix(1)}]\cr correlation list output from the function \code{\link[corrp]{corrp}}
-with class \code{\link[corrp]{clist}} or correlation matrix output
-from \code{\link[corrp]{corr_matrix}} with class \code{\link[corrp]{cmatrix}}.}
+with class \code{clist} or correlation matrix output
+from \code{\link[corrp]{corr_matrix}} with class \code{cmatrix}.}
 
 \item{...}{Additional arguments.}
 
@@ -62,7 +62,7 @@ corr_rm(df = iris, c = iris_cmatrix, cutoff = 0.75, col = "infer.value", isig =
 
 }
 \author{
-Igor D.S. Siciliani
+Igor D.S. Siciliani, Paulo H. dos Santos
 }
 \keyword{,}
 \keyword{clist}
diff --git a/man/corrp.Rd b/man/corrp.Rd
index f511ea8..a85c229 100644
--- a/man/corrp.Rd
+++ b/man/corrp.Rd
@@ -21,7 +21,7 @@ corrp(
   pearson.args = list(),
   dcor.args = list(),
   mic.args = list(),
-  pps.args = list(),
+  pps.args = list(ptest = FALSE),
   cramersV.args = list(),
   uncoef.args = list(),
   ...
@@ -67,19 +67,19 @@ Choose correlation type to be used in factor/categorical pair inference.
 The option are \verb{cramersV: Cramer's V},\verb{uncoef: Uncertainty coefficient},
 \verb{pps: Predictive Power Score}. Default is \verb{ Cramer's V}.}
 
-\item{lm.args}{[\code{list(1)}]\cr additional parameters for the specific method.}
+\item{lm.args}{[\code{list(1)}]\cr additional parameters for linear model to be passed to \code{\link[stats]{lm}}.}
 
-\item{pearson.args}{[\code{list(1)}]\cr additional parameters for the specific method.}
+\item{pearson.args}{[\code{list(1)}]\cr additional parameters for Pearson correlation to be passed to \code{\link[stats]{cor.test}}.}
 
-\item{dcor.args}{[\code{list(1)}]\cr additional parameters for the specific method.}
+\item{dcor.args}{[\code{list(1)}]\cr additional parameters for the distance correlation to be passed to \code{\link[corrp]{dcorT_test}}.}
 
-\item{mic.args}{[\code{list(1)}]\cr additional parameters for the specific method.}
+\item{mic.args}{[\code{list(1)}]\cr additional parameters for the maximal information coefficient to be passed to \code{\link[minerva]{mine}}.}
 
-\item{pps.args}{[\code{list(1)}]\cr additional parameters for the specific method.}
+\item{pps.args}{[\code{list(1)}]\cr additional parameters for the predictive power score to be passed to \code{\link[ppsr]{score}}.}
 
-\item{cramersV.args}{[\code{list(1)}]\cr additional parameters for the specific method.}
+\item{cramersV.args}{[\code{list(1)}]\cr additional parameters for the Cramer's V to be passed to \code{\link[lsr]{cramersV}}.}
 
-\item{uncoef.args}{[\code{list(1)}]\cr additional parameters for the specific method.}
+\item{uncoef.args}{[\code{list(1)}]\cr additional parameters for the uncertainty coefficient to be passed to \code{\link[DescTools]{UncertCoef}}.}
 
 \item{...}{Additional arguments.}
 }
@@ -103,9 +103,10 @@ A list with two tables: \code{data} and \code{index}.
 All statistical tests are controlled by the confidence internal of
 p.value param. If the statistical tests do not obtain a significance greater/less
 than p.value the value of variable \code{isig} will be \code{FALSE}.\cr
-There is no statistical significance test for the pps algorithm. By default \code{isig} is TRUE.\cr
 If any errors occur during operations the association measure (\code{infer.value}) will be \code{NA}.\cr
 The result \code{data} and \code{index} will have \eqn{N^2} rows, where N is the number of variables of the input data.
+By default there is no statistical significance test for the pps algorithm. By default \code{isig} is NA, you can enable in the \code{pps.args} setting \code{ptest = TRUE}.\cr
+All the \verb{*.args} can modified the parameters (\code{p.value}, \code{comp}, \code{alternative}, \code{num.s}, \code{rk}, \code{ptest}) for the respective method on it's prefix.
 }
 \description{
 Compute correlations type analysis on mixed classes columns of larges dataframes
@@ -114,37 +115,35 @@ The dataframe is allowed to have columns of these four classes: integer,
 numeric, factor and character. The character column is considered as
 categorical variable.
 }
-\section{Details (Pair Types)}{
+\section{Pair Types}{
 
+
+\strong{Numeric pairs (integer/numeric):}
+\itemize{
+\item \strong{Pearson Correlation Coefficient:} A widely used measure of the strength and direction of linear relationships. Implemented using \code{\link[stats]{cor}}. For more details, see \url{https://doi.org/10.1098/rspl.1895.0041}. The value lies between -1 and 1.\cr
+\item \strong{Distance Correlation:} Based on the idea of expanding covariance to distances, it measures both linear and nonlinear associations between variables. Implemented using \code{\link[energy]{dcorT.test}}. For more details, see \url{https://doi.org/10.1214/009053607000000505}. The value lies between 0 and 1.\cr
+\item \strong{Maximal Information Coefficient (MIC):} An information-based nonparametric method that can detect both linear and non-linear relationships between variables. Implemented using \code{\link[minerva]{mine}}. For more details, see \url{https://doi.org/10.1126/science.1205438}. The value lies between 0 and 1.\cr
+\item \strong{Predictive Power Score (PPS):} A metric used to assess predictive relations between variables. Implemented using \code{\link[ppsr]{score}}. For more details, see \url{https://zenodo.org/record/4091345}. The value lies between 0 and 1.\cr\cr
+}
+
+\strong{Numeric and categorical pairs (integer/numeric - factor/categorical):}
+\itemize{
+\item \strong{Square Root of R² Coefficient:} From linear regression of the numeric variable over the categorical variable. Implemented using \code{\link[stats]{lm}}. For more details, see \url{https://doi.org/10.4324/9780203774441}. The value lies between 0 and 1.\cr
+\item \strong{Predictive Power Score (PPS):} A metric used to assess predictive relations between numeric and categorical variables. Implemented using \code{\link[ppsr]{score}}. For more details, see \url{https://zenodo.org/record/4091345}. The value lies between 0 and 1.\cr\cr
+}
+
+\strong{Categorical pairs (factor/categorical):}
 \itemize{
-\item \code{integer/numeric pair} Pearson Correlation using \code{\link[stats]{cor}} function. The
-value lies between -1 and 1.\cr
-\item \code{integer/numeric pair} Distance Correlation using \code{\link[energy]{dcorT.test}} function. The
-value lies between 0 and 1.\cr
-\item \code{integer/numeric pair} Maximal Information Coefficient using \code{\link[minerva]{mine}} function. The
-value lies between 0 and 1.\cr
-\item \code{integer/numeric pair} Predictive Power Score using \code{\link[ppsr]{score}} function. The
-value lies between 0 and 1.\cr\cr
-\item \code{integer/numeric - factor/categorical pair} correlation coefficient or
-squared root of R^2 coefficient of linear regression of integer/numeric
-variable over factor/categorical variable using \code{\link[stats]{lm}} function. The value
-lies between 0 and 1.\cr
-\item \code{integer/numeric - factor/categorical pair} Predictive Power Score using \code{\link[ppsr]{score}} function. The
-value lies between 0 and 1.\cr\cr
-\item \code{factor/categorical pair} Cramer's V value is
-computed based on chisq test and using \code{\link[lsr]{cramersV}} function. The value lies
-between 0 and 1.\cr
-\item \code{factor/categorical pair} Uncertainty coefficient using \code{\link[DescTools]{UncertCoef}} function. The
-value lies between 0 and 1.\cr
-\item \code{factor/categorical pair} Predictive Power Score using \code{\link[ppsr]{score}} function. The
-value lies between 0 and 1.\cr
+\item \strong{Cramér's V:} A measure of association between nominal variables. Computed based on a chi-squared test and implemented using \code{\link[lsr]{cramersV}}. For more details, see \url{https://doi.org/10.1515/9781400883868}. The value lies between 0 and 1.\cr
+\item \strong{Uncertainty Coefficient:} A measure of nominal association between two variables. Implemented using \code{\link[DescTools]{UncertCoef}}. For more details, see \url{https://doi.org/10.1016/j.jbi.2010.02.001}. The value lies between 0 and 1.\cr
+\item \strong{Predictive Power Score (PPS):} A metric used to assess predictive relations between categorical variables. Implemented using \code{\link[ppsr]{score}}. For more details, see \url{https://zenodo.org/record/4091345}. The value lies between 0 and 1.\cr
 }
 }
 
 \examples{
  iris_c <- corrp(iris)
  iris_m <- corr_matrix(iris_c, isig = FALSE)
- corrplot(iris_m)
+ corrplot::corrplot(iris_m)
 
 
 }
@@ -156,7 +155,7 @@ Paul van der Laken, ppsr,2021.
 URL \url{https://github.com/paulvanderlaken/ppsr}.
 }
 \author{
-Igor D.S. Siciliani
+Igor D.S. Siciliani, Paulo H. dos Santos
 }
 \keyword{,}
 \keyword{biserial}
diff --git a/man/set_arguments.Rd b/man/set_arguments.Rd
new file mode 100644
index 0000000..06326be
--- /dev/null
+++ b/man/set_arguments.Rd
@@ -0,0 +1,18 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/utils.R
+\name{set_arguments}
+\alias{set_arguments}
+\title{Set Argument}
+\usage{
+set_arguments(args_list)
+}
+\arguments{
+\item{args_list}{[\code{list}]\cr
+A named list of arguments to be assigned to the parent environment.}
+}
+\value{
+A modified \code{args_list} with the arguments that were assigned to the parent environment removed.
+}
+\description{
+Assigns provided arguments from the \code{args_list} to the parent environment. If an argument is inside the arguments of the methods that calculate statistics, it assigns it on the parent environment, and removes the argument from the list.
+}
diff --git a/man/sil_acca.Rd b/man/sil_acca.Rd
index 4cd0770..cbfdd94 100644
--- a/man/sil_acca.Rd
+++ b/man/sil_acca.Rd
@@ -47,7 +47,7 @@ Starczewski, Artur, and Adam Krzyżak. "Performance evaluation of the silhouette
 " International Conference on Artificial Intelligence and Soft Computing. Springer, Cham, 2015.
 }
 \author{
-Igor D.S. Siciliani
+Igor D.S. Siciliani, Paulo H. dos Santos
 }
 \keyword{,}
 \keyword{acca}