diff --git a/R-package/R/xgb.cv.R b/R-package/R/xgb.cv.R
index e5f5c7b72..2dee9d9f8 100644
--- a/R-package/R/xgb.cv.R
+++ b/R-package/R/xgb.cv.R
@@ -54,6 +54,13 @@
 #' @param folds \code{list} provides a possibility of using a list of pre-defined CV folds (each element must be a vector of fold's indices).
 #'   If folds are supplied, the nfold and stratified parameters would be ignored.
 #' @param verbose \code{boolean}, print the statistics during the process
+#' @param early_stop_round If \code{NULL}, the early stopping function is not triggered. 
+#'     If set to an integer \code{k}, training with a validation set will stop if the performance 
+#'     keeps getting worse consecutively for \code{k} rounds.
+#' @param early.stop.round An alternative of \code{early_stop_round}.
+#' @param maximize If \code{feval} and \code{early_stop_round} are set, then \code{maximize} must be set as well.
+#'     \code{maximize=TRUE} means the larger the evaluation score the better.
+#'     
 #' @param ... other parameters to pass to \code{params}.
 #' 
 #' @return
@@ -86,7 +93,8 @@
 #'
 xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing = NULL, 
                    prediction = FALSE, showsd = TRUE, metrics=list(), 
-                   obj = NULL, feval = NULL, stratified = TRUE, folds = NULL, verbose = T,...) {
+                   obj = NULL, feval = NULL, stratified = TRUE, folds = NULL, verbose = T,
+                   early_stop_round = NULL, early.stop.round = NULL, maximize = NULL, ...) {
   if (typeof(params) != "list") {
     stop("xgb.cv: first argument params must be list")
   }
@@ -109,7 +117,36 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
   for (mc in metrics) {
     params <- append(params, list("eval_metric"=mc))
   }
-
+  
+  # Early Stopping
+  if (is.null(early_stop_round) && !is.null(early.stop.round))
+    early_stop_round = early.stop.round
+  if (!is.null(early_stop_round)){
+    if (!is.null(feval) && is.null(maximize))
+      stop('Please set maximize to note whether the model is maximizing the evaluation or not.')
+    if (is.null(maximize) && is.null(params$eval_metric))
+      stop('Please set maximize to note whether the model is maximizing the evaluation or not.')
+    if (is.null(maximize))
+    {
+      if (params$eval_metric %in% c('rmse','logloss','error','merror','mlogloss')) {
+        maximize = FALSE
+      } else {
+        maximize = TRUE
+      }
+    }
+    
+    if (maximize) {
+      bestScore = 0
+    } else {
+      bestScore = Inf
+    }
+    bestInd = 0
+    earlyStopflag = FALSE
+    
+    if (length(metrics)>1)
+      warning('Only the first metric is used for early stopping process.')
+  }
+  
   xgb_folds <- xgb.cv.mknfold(dtrain, nfold, params, stratified, folds)
   obj_type = params[['objective']]
   mat_pred = FALSE
@@ -149,6 +186,24 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
     ret <- xgb.cv.aggcv(msg, showsd)
     history <- c(history, ret)
     if(verbose) paste(ret, "\n", sep="") %>% cat
+    
+    # early_Stopping
+    if (!is.null(early_stop_round)){
+      score = strsplit(ret,'\\s+')[[1]][1+length(metrics)+1]
+      score = strsplit(score,'\\+|:')[[1]][[2]]
+      score = as.numeric(score)
+      if ((maximize && score>bestScore) || (!maximize && score<bestScore)) {
+        bestScore = score
+        bestInd = i
+      } else {
+        if (i-bestInd>early_stop_round) {
+          earlyStopflag = TRUE
+          cat('Stopping. Best iteration:',bestInd)
+          break
+        }
+      }
+    }
+    
   }
   
   colnames <- str_split(string = history[1], pattern = "\t")[[1]] %>% .[2:length(.)] %>% str_extract(".*:") %>% str_replace(":","") %>% str_replace("-", ".")
diff --git a/R-package/demo/early_Stopping.R b/R-package/demo/early_Stopping.R
index 4cab385ca..692b413aa 100644
--- a/R-package/demo/early_Stopping.R
+++ b/R-package/demo/early_Stopping.R
@@ -35,3 +35,5 @@ print ('start training with early Stopping setting')
 # simply look at xgboost.py's implementation of train
 bst <- xgb.train(param, dtrain, num_round, watchlist, logregobj, evalerror, maximize = FALSE,
                  earlyStopRound = 3)
+bst <- xgb.cv(param, dtrain, num_round, nfold=5, obj=logregobj, feval = evalerror,
+              maximize = FALSE, earlyStopRound = 3)
diff --git a/R-package/man/xgb.cv.Rd b/R-package/man/xgb.cv.Rd
index 19ab788f9..a2cd68c92 100644
--- a/R-package/man/xgb.cv.Rd
+++ b/R-package/man/xgb.cv.Rd
@@ -7,7 +7,8 @@
 xgb.cv(params = list(), data, nrounds, nfold, label = NULL,
   missing = NULL, prediction = FALSE, showsd = TRUE, metrics = list(),
   obj = NULL, feval = NULL, stratified = TRUE, folds = NULL,
-  verbose = T, ...)
+  verbose = T, early_stop_round = NULL, early.stop.round = NULL,
+  maximize = NULL, ...)
 }
 \arguments{
 \item{params}{the list of parameters. Commonly used ones are:
@@ -65,6 +66,15 @@ If folds are supplied, the nfold and stratified parameters would be ignored.}
 
 \item{verbose}{\code{boolean}, print the statistics during the process}
 
+\item{early_stop_round}{If \code{NULL}, the early stopping function is not triggered.
+If set to an integer \code{k}, training with a validation set will stop if the performance
+keeps getting worse consecutively for \code{k} rounds.}
+
+\item{early.stop.round}{An alternative of \code{early_stop_round}.}
+
+\item{maximize}{If \code{feval} and \code{early_stop_round} are set, then \code{maximize} must be set as well.
+    \code{maximize=TRUE} means the larger the evaluation score the better.}
+
 \item{...}{other parameters to pass to \code{params}.}
 }
 \value{