diff --git a/R/corrp.R b/R/corrp.R
index 2f21a40..5f28f46 100644
--- a/R/corrp.R
+++ b/R/corrp.R
@@ -39,7 +39,8 @@
 #'   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
 #' - There is no statistical significance test for the pps algorithm. By default `isig` is TRUE.\cr
-#' - If any errors occur during operations the association measure(`infer.value`) will be `NA`.
+#' - 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.
 #'
 #' @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.
diff --git a/man/corrp.Rd b/man/corrp.Rd
index a3742d4..cf87330 100644
--- a/man/corrp.Rd
+++ b/man/corrp.Rd
@@ -91,7 +91,8 @@ list with two tables: data and index.\cr
   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
 - There is no statistical significance test for the pps algorithm. By default `isig` is TRUE.\cr
-- If any errors occur during operations the association measure(`infer.value`) will be `NA`.
+- 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.
 }
 \description{
 Compute correlations type analysis on mixed classes columns of larges dataframes