diff --git a/R-package/R/xgb.train.R b/R-package/R/xgb.train.R
index 6f6c1a900..636ad3cad 100644
--- a/R-package/R/xgb.train.R
+++ b/R-package/R/xgb.train.R
@@ -139,11 +139,11 @@ xgb.train <- function(params=list(), data, nrounds, watchlist = list(),
   params = append(params, list(...))
   
   # Early stopping
-  if (!is.null(feval) && is.null(maximize))
+  if (!is.null(feval) && is.null(maximize) && !is.null(earlyStopRound))
     stop('Please set maximize to note whether the model is maximizing the evaluation or not.')
   if (length(watchlist) == 0 && !is.null(earlyStopRound))
     stop('For early stopping you need at least one set in watchlist.')
-  if (is.null(maximize) && is.null(params$eval_metric))
+  if (is.null(maximize) && is.null(params$eval_metric) && !is.null(earlyStopRound))
     stop('Please set maximize to note whether the model is maximizing the evaluation or not.')
   if (is.null(maximize))
   {
diff --git a/R-package/demo/00Index b/R-package/demo/00Index
index 969da0d91..f0b41ec2a 100644
--- a/R-package/demo/00Index
+++ b/R-package/demo/00Index
@@ -6,3 +6,4 @@ generalized_linear_model        Generalized Linear Model
 cross_validation                Cross validation
 create_sparse_matrix            Create Sparse Matrix
 predict_leaf_indices            Predicting the corresponding leaves
+early_Stopping                  Early Stop in training
diff --git a/R-package/demo/early_Stopping.R b/R-package/demo/early_Stopping.R
new file mode 100644
index 000000000..3253c3828
--- /dev/null
+++ b/R-package/demo/early_Stopping.R
@@ -0,0 +1,58 @@
+require(xgboost)
+# load in the agaricus dataset
+data(agaricus.train, package='xgboost')
+data(agaricus.test, package='xgboost')
+dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
+dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
+# note: for customized objective function, we leave objective as default
+# note: what we are getting is margin value in prediction
+# you must know what you are doing
+param <- list(max.depth=2,eta=1,nthread = 2, silent=1)
+watchlist <- list(eval = dtest)
+num_round <- 20
+# user define objective function, given prediction, return gradient and second order gradient
+# this is loglikelihood loss
+logregobj <- function(preds, dtrain) {
+  labels <- getinfo(dtrain, "label")
+  preds <- 1/(1 + exp(-preds))
+  grad <- preds - labels
+  hess <- preds * (1 - preds)
+  return(list(grad = grad, hess = hess))
+}
+# user defined evaluation function, return a pair metric_name, result
+# NOTE: when you do customized loss function, the default prediction value is margin
+# this may make buildin evalution metric not function properly
+# for example, we are doing logistic loss, the prediction is score before logistic transformation
+# the buildin evaluation error assumes input is after logistic transformation
+# Take this in mind when you use the customization, and maybe you need write customized evaluation function
+evalerror <- function(preds, dtrain) {
+  labels <- getinfo(dtrain, "label")
+  err <- as.numeric(sum(labels != (preds > 0)))/length(labels)
+  return(list(metric = "error", value = err))
+}
+print ('start training with user customized objective')
+# training with customized objective, we can also do step by step training
+# simply look at xgboost.py's implementation of train
+bst <- xgb.train(param, dtrain, num_round, watchlist, logregobj, evalerror, maximize = FALSE,
+                 earlyStopRound = 3)
+#
+# there can be cases where you want additional information
+# being considered besides the property of DMatrix you can get by getinfo
+# you can set additional information as attributes if DMatrix
+# set label attribute of dtrain to be label, we use label as an example, it can be anything
+attr(dtrain, 'label') <- getinfo(dtrain, 'label')
+# this is new customized objective, where you can access things you set
+# same thing applies to customized evaluation function
+logregobjattr <- function(preds, dtrain) {
+  # now you can access the attribute in customized function
+  labels <- attr(dtrain, 'label')
+  preds <- 1/(1 + exp(-preds))
+  grad <- preds - labels
+  hess <- preds * (1 - preds)
+  return(list(grad = grad, hess = hess))
+}
+print ('start training with user customized objective, with additional attributes in DMatrix')
+# training with customized objective, we can also do step by step training
+# simply look at xgboost.py's implementation of train
+bst <- xgb.train(param, dtrain, num_round, watchlist, logregobjattr, evalerror, maximize = FALSE,
+                 earlyStopRound = 3)
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