Remarks regarding JOSS paper #34

make a lot of example for corrp

R/corrp.R

@@ -100,10 +100,30 @@
 #' Paul van der Laken, ppsr, 2021. URL: \url{https://github.com/paulvanderlaken/ppsr/}.
 #'
 #' @examples
+#' # Usage with default settings
 #' iris_c <- corrp(iris)
-#' iris_m <- corr_matrix(iris_c, isig = FALSE)
-#' corrplot::corrplot(iris_m)
+#' iris_m <- corr_matrix(iris_c, isig = FALSE) # You can then make correlation matrix
+#' corrp::corrplot(iris_m) # You can visualize the matrix using corrplot
 #'
+#' # Using PPS for both numeric-numeric and numeric-categorical pairs
+#' iris_c1 <- corrp(iris, cor.nn = "pps", cor.nc = "pps")
+#'
+#' # Using Distance Correlation for numeric-numeric and Predictive Power Score for numeric-categorical
+#' iris_c2 <- corrp(iris, cor.nn = "dcor", cor.nc = "pps", dcor.args = list(method = "auto"))
+#'
+#' # Using Maximal Information Coefficient (MIC) for numeric-numeric and Uncertainty Coefficient for categorical-categorical
+#' iris_c3 <- corrp(iris, cor.nn = "mic", cor.cc = "uncoef", mic.args = list(alpha = 0.6))
+#'
+#' # Using PPS for all pair types
+#' iris_c4 <- corrp(iris, cor.nn = "pps", cor.nc = "pps", cor.cc = "pps")
+#'
+#' # Using Distance Correlation for numeric-numeric, Predictive Power Score for numeric-categorical,
+#' # and Uncertainty Coefficient for categorical-categorical
+#' iris_c5 <- corrp(
+#'   iris,
+#'   cor.nn = "dcor", cor.nc = "pps", cor.cc = "uncoef",
+#'   dcor.args = list(method = "auto")
+#' )
 #' @export
 corrp <- function(df,
                   parallel = TRUE,