diff --git a/R/kNNFaster.R b/R/kNNFaster.R
index 8594cde..0265be5 100644
--- a/R/kNNFaster.R
+++ b/R/kNNFaster.R
@@ -21,13 +21,8 @@
 #' k-Nearest Neighbour Imputation based on a variation of the Gower Distance
 #' for numerical, categorical, ordered and semi-continous variables.
 #' 
-#' The function sampleCat samples with probabilites corresponding to the
-#' occurrence of the level in the NNs. The function maxCat chooses the level
-#' with the most occurrences and random if the maximum is not unique. The
-#' function gowerD is used by kNN to compute the distances for numerical,
-#' factor ordered and semi-continous variables. 
 #' 
-#' @aliases kNN sampleCat maxCat gowerD
+#' @aliases kNN
 #' @param data data.frame or matrix
 #' @param variable variables where missing values should be imputed
 #' @param metric metric to be used for calculating the distances between
@@ -140,48 +135,14 @@ lengthL <- function(x){
   }
 }
 
-#' @rdname kNN
-#' @export 
-#' @param x factor vector related to function sampleCat
- 
-sampleCat <- function(x,weights = NULL){
-  #sample with probabilites corresponding to there number in the NNs
-  if(!is.factor(x))
-    x <- as.factor(x)
-  s <- summary(x)
-  s <- s[s!=0]
-  if(!is.null(weights)){
-    tmpTab <- merge(aggregate(weights,list(x),sum), data.frame("Group.1"=names(s),prob=s))
-    s <- tmpTab$prob*tmpTab$x
-    names(s) <- tmpTab$Group.1
-  }
-  
-  sample(names(s),1,prob=s)
-}
 
-#' @rdname kNN
-#' @export maxCat
 
-maxCat <- function(x,weights = NULL){
-  #choose cat with max prob, random if max is not unique
-  if(!is.factor(x))
-    x <- as.factor(x)
-  s <- summary(x)
-  s <- s[s!=0]
-  if(!is.null(weights)){
-    tmpTab <- merge(aggregate(weights,list(x),sum), data.frame("Group.1"=names(s),prob=s))
-    s <- tmpTab$prob*tmpTab$x
-    names(s) <- tmpTab$Group.1
-  }
-  if(sum(s>0)>1)
-    s <- sample(s)
-  names(s)[which.max(s)]
-}
 dist_single <- function(don_dist_var,imp_dist_var,numericalX,factorsX,ordersX,mixedX,levOrdersX,
                         don_index,imp_index,weightsx,k,mixed.constant,provideMins=TRUE){
   #gd <- distance(don_dist_var,imp_dist_var,weights=weightsx)
   if(is.null(mixed.constant))
     mixed.constant <- rep(0,length(mixedX))
+  
   if(provideMins){
     gd <- gowerD(don_dist_var,imp_dist_var,weights=weightsx,numericalX,
                  factorsX,ordersX,mixedX,levOrdersX,mixed.constant=mixed.constant,returnIndex=TRUE,
diff --git a/man/kNN.Rd b/man/kNN.Rd
index 65fa218..39a67c8 100644
--- a/man/kNN.Rd
+++ b/man/kNN.Rd
@@ -2,9 +2,6 @@
 % Please edit documentation in R/kNNFaster.R
 \name{kNN}
 \alias{kNN}
-\alias{sampleCat}
-\alias{maxCat}
-\alias{gowerD}
 \alias{kNN.data.table}
 \alias{kNN.data.frame}
 \alias{kNN.survey.design}
@@ -135,10 +132,6 @@ kNN(
   useImputedDist = TRUE,
   weightDist = FALSE
 )
-
-sampleCat(x, weights = NULL)
-
-maxCat(x, weights = NULL)
 }
 \arguments{
 \item{data}{data.frame or matrix}
@@ -196,8 +189,6 @@ Be aware that this results in a dependency on the ordering of the variables.}
 
 \item{weightDist}{TRUE/FALSE if the distances of the k nearest neighbours should be used as weights in the
 aggregation step}
-
-\item{x}{factor vector related to function sampleCat}
 }
 \value{
 the imputed data set.
@@ -206,13 +197,6 @@ the imputed data set.
 k-Nearest Neighbour Imputation based on a variation of the Gower Distance
 for numerical, categorical, ordered and semi-continous variables.
 }
-\details{
-The function sampleCat samples with probabilites corresponding to the
-occurrence of the level in the NNs. The function maxCat chooses the level
-with the most occurrences and random if the maximum is not unique. The
-function gowerD is used by kNN to compute the distances for numerical,
-factor ordered and semi-continous variables.
-}
 \examples{
 
 data(sleep)