diff --git a/R/wrapper.R b/R/wrapper.R
index cf276b8..88d4459 100644
--- a/R/wrapper.R
+++ b/R/wrapper.R
@@ -1,40 +1,60 @@
 #' Performs ranking via aggregate-then-rank
 #'
 #' Performs ranking by first aggregating performance values across all cases (e.g., with the mean, median or another quantile) for each algorithm.
 #' This aggregate is then used to compute a rank for each algorithm.
 #'
 #' @param object The challenge object.
 #' @param FUN The aggregation function, e.g. mean, median, min, max, function(x), quantile(x, probs=0.05).
 #' @param ties.method A string specifying how ties are treated, see \code{\link{base::rank}}.
 #'
 #' @return An S3 object of class "ranked.list" to represent a ranked assessment data set.
 #'
 #' @examples
 #'
 #' \dontrun{
 #'  aggregateThenRank(challenge, FUN = mean, ties.method="average", na.treat = 0)
 #' }
 #'
 #' @family ranking functions
 #' @export
 aggregateThenRank=function(object,FUN,ties.method = "min",...){
   object %>%
     aggregate(FUN=FUN,...) %>%
     rank(ties.method = ties.method)
 }
 
 testThenRank=function(object, ties.method = "min",...){
   object %>%
     aggregate(FUN="significance",...) %>%
     rank(ties.method = ties.method)
 }
 
+#' Performs ranking via rank-then-aggregate
+#'
+#' Performs ranking by first computing a rank for each case for each algorithm (”rank first”).
+#' The final rank is based on the aggregated ranks for the cases. This ranking method handles missing values implicitly
+#' by assigning the worst rank to missing algorithm performances.
+#'
+#'
+#' @param object The challenge object.
+#' @param FUN The aggregation function, e.g., mean, median, min, max, function(x), quantile(x, probs=0.05).
+#' @param ties.method A string specifying how ties are treated, see \code{\link{base::rank}}.
+#'
+#' @return An S3 object of class "ranked.list" to represent a ranked assessment data set.
+#'
+#' @examples
+#' \dontrun{
+#'  rankThenAggregate(challenge, FUN = mean)
+#' }
+#'
+#' @family ranking functions
+#' @export
 rankThenAggregate=function(object,
                            FUN,
                            ties.method = "min"
                            ){
   object %>%
         rank(ties.method = ties.method)%>%
           aggregate(FUN=FUN) %>%
           rank(ties.method = ties.method) # small rank is always best, i.e. smallBetter always TRUE
 }