diff --git a/R-package/NAMESPACE b/R-package/NAMESPACE
index f8b409724..09924aa05 100644
--- a/R-package/NAMESPACE
+++ b/R-package/NAMESPACE
@@ -1,11 +1,2 @@
-importClassesFrom("Matrix", dgCMatrix, dgeMatrix)
+# Generated by roxygen2 (4.0.1): do not edit by hand
 
-export(xgboost)
-export(xgb.DMatrix)
-exportMethods(predict)
-exportMethods(getinfo)
-export(xgb.train)
-export(xgb.save)
-export(xgb.load)
-export(xgb.dump)
-export(xgb.DMatrix.save)
diff --git a/R-package/R/getinfo.xgb.DMatrix.R b/R-package/R/getinfo.xgb.DMatrix.R
index 6fe931808..52be489e1 100644
--- a/R-package/R/getinfo.xgb.DMatrix.R
+++ b/R-package/R/getinfo.xgb.DMatrix.R
@@ -7,9 +7,6 @@ setClass('xgb.DMatrix')
 #' @param object Object of class "xgb.DMatrix"
 #' @param name the name of the field to get
 #' 
-#' @section Value
-#' return a numerical vector.
-#' 
 #' @examples
 #' data(iris)
 #' iris[,5] <- as.numeric(iris[,5])
diff --git a/R-package/R/predict.xgb.Booster.R b/R-package/R/predict.xgb.Booster.R
index 0cec490dc..2633c081b 100644
--- a/R-package/R/predict.xgb.Booster.R
+++ b/R-package/R/predict.xgb.Booster.R
@@ -11,9 +11,6 @@ setClass("xgb.Booster")
 #'   value of sum of functions, when outputmargin=TRUE, the prediction is 
 #'   untransformed margin value. In logistic regression, outputmargin=T will
 #'   output value before logistic transformation.
-#'
-#' @section Value
-#' return a numerical vector.
 #' 
 #' @examples
 #' data(iris)
diff --git a/R-package/R/xgb.train.R b/R-package/R/xgb.train.R
index c0a0278ae..d006ae886 100644
--- a/R-package/R/xgb.train.R
+++ b/R-package/R/xgb.train.R
@@ -36,8 +36,6 @@
 #' It supports advanced features such as watchlist, customized objective function,
 #' therefore it is more flexible than \code{\link{xgboost}}.
 #' 
-#' @section Value
-#' return a \code{xgb.DMatrix} class object.
 #' 
 #' @examples
 #' data(iris)
diff --git a/R-package/R/xgboost.R b/R-package/R/xgboost.R
index 9c2d05fc9..3a89d96d4 100644
--- a/R-package/R/xgboost.R
+++ b/R-package/R/xgboost.R
@@ -28,9 +28,6 @@
 #' Parallelization is automatically enabled if OpenMP is present.
 #' Number of threads can also be manually specified via "nthread" parameter
 #' 
-#' @section Value
-#' return a \code{xgb.DMatrix} class object.
-#' 
 #' @examples
 #' data(iris)
 #' bst <- xgboost(as.matrix(iris[,1:4]),as.numeric(iris[,5]), nrounds = 2)
diff --git a/R-package/man/getinfo.Rd b/R-package/man/getinfo.Rd
new file mode 100644
index 000000000..beee4f850
--- /dev/null
+++ b/R-package/man/getinfo.Rd
@@ -0,0 +1,22 @@
+% Generated by roxygen2 (4.0.1): do not edit by hand
+\name{getinfo}
+\alias{getinfo}
+\title{Get information of an xgb.DMatrix object}
+\usage{
+getinfo(object, ...)
+}
+\arguments{
+\item{object}{Object of class "xgb.DMatrix"}
+
+\item{name}{the name of the field to get}
+}
+\description{
+Get information of an xgb.DMatrix object
+}
+\examples{
+data(iris)
+iris[,5] <- as.numeric(iris[,5])
+dtrain <- xgb.DMatrix(as.matrix(iris[,1:4]), label=iris[,5])
+labels <- getinfo(dtest, "label")
+}
+
diff --git a/R-package/man/predict-xgb.Booster-method.Rd b/R-package/man/predict-xgb.Booster-method.Rd
new file mode 100644
index 000000000..d43fd7362
--- /dev/null
+++ b/R-package/man/predict-xgb.Booster-method.Rd
@@ -0,0 +1,28 @@
+% Generated by roxygen2 (4.0.1): do not edit by hand
+\docType{methods}
+\name{predict,xgb.Booster-method}
+\alias{predict,xgb.Booster-method}
+\title{Predict method for eXtreme Gradient Boosting model}
+\usage{
+\S4method{predict}{xgb.Booster}(object, newdata, outputmargin = FALSE)
+}
+\arguments{
+\item{object}{Object of class "xgb.Boost"}
+
+\item{newdata}{takes \code{matrix}, \code{dgCMatrix}, local data file or
+\code{xgb.DMatrix}.}
+
+\item{outputmargin}{whether the prediction should be shown in the original
+  value of sum of functions, when outputmargin=TRUE, the prediction is
+  untransformed margin value. In logistic regression, outputmargin=T will
+  output value before logistic transformation.}
+}
+\description{
+Predicted values based on xgboost model object.
+}
+\examples{
+data(iris)
+bst <- xgboost(as.matrix(iris[,1:4]),as.numeric(iris[,5]), nrounds = 2)
+pred <- predict(bst, as.matrix(iris[,1:4]))
+}
+
diff --git a/R-package/man/xgb.DMatrix.Rd b/R-package/man/xgb.DMatrix.Rd
new file mode 100644
index 000000000..166d69f68
--- /dev/null
+++ b/R-package/man/xgb.DMatrix.Rd
@@ -0,0 +1,28 @@
+% Generated by roxygen2 (4.0.1): do not edit by hand
+\name{xgb.DMatrix}
+\alias{xgb.DMatrix}
+\title{Contruct xgb.DMatrix object}
+\usage{
+xgb.DMatrix(data, info = list(), missing = 0, ...)
+}
+\arguments{
+\item{data}{a \code{matrix} object, a \code{dgCMatrix} object or a character
+indicating the data file.}
+
+\item{info}{a list of information of the xgb.DMatrix object}
+
+\item{missing}{Missing is only used when input is dense matrix, pick a float}
+
+\item{...}{other information to pass to \code{info}.}
+}
+\description{
+Contruct xgb.DMatrix object from dense matrix, sparse matrix or local file.
+}
+\examples{
+data(iris)
+iris[,5] <- as.numeric(iris[,5])
+dtrain <- xgb.DMatrix(as.matrix(iris[,1:4]), label=iris[,5])
+xgb.DMatrix.save(dtrain, 'iris.xgb.DMatrix')
+dtrain <- xgb.DMatrix('iris.xgb.DMatrix')
+}
+
diff --git a/R-package/man/xgb.DMatrix.save.Rd b/R-package/man/xgb.DMatrix.save.Rd
new file mode 100644
index 000000000..d4932fa42
--- /dev/null
+++ b/R-package/man/xgb.DMatrix.save.Rd
@@ -0,0 +1,23 @@
+% Generated by roxygen2 (4.0.1): do not edit by hand
+\name{xgb.DMatrix.save}
+\alias{xgb.DMatrix.save}
+\title{Save xgb.DMatrix object to binary file}
+\usage{
+xgb.DMatrix.save(DMatrix, fname)
+}
+\arguments{
+\item{model}{the model object.}
+
+\item{fname}{the name of the binary file.}
+}
+\description{
+Save xgb.DMatrix object to binary file
+}
+\examples{
+data(iris)
+iris[,5] <- as.numeric(iris[,5])
+dtrain <- xgb.DMatrix(as.matrix(iris[,1:4]), label=iris[,5])
+xgb.DMatrix.save(dtrain, 'iris.xgb.DMatrix')
+dtrain <- xgb.DMatrix('iris.xgb.DMatrix')
+}
+
diff --git a/R-package/man/xgb.dump.Rd b/R-package/man/xgb.dump.Rd
new file mode 100644
index 000000000..1e0360b31
--- /dev/null
+++ b/R-package/man/xgb.dump.Rd
@@ -0,0 +1,25 @@
+% Generated by roxygen2 (4.0.1): do not edit by hand
+\name{xgb.dump}
+\alias{xgb.dump}
+\title{Save xgboost model to text file}
+\usage{
+xgb.dump(model, fname, fmap = "")
+}
+\arguments{
+\item{model}{the model object.}
+
+\item{fname}{the name of the binary file.}
+
+\item{fmap}{feature map file representing the type of feature, to make it
+       look nice, run demo/demo.R for result and demo/featmap.txt for example
+       Format: https://github.com/tqchen/xgboost/wiki/Binary-Classification#dump-model}
+}
+\description{
+Save a xgboost model to text file. Could be parsed later.
+}
+\examples{
+data(iris)
+bst <- xgboost(as.matrix(iris[,1:4]),as.numeric(iris[,5]), nrounds = 2)
+xgb.dump(bst, 'iris.xgb.model.dump')
+}
+
diff --git a/R-package/man/xgb.load.Rd b/R-package/man/xgb.load.Rd
new file mode 100644
index 000000000..980daf88d
--- /dev/null
+++ b/R-package/man/xgb.load.Rd
@@ -0,0 +1,21 @@
+% Generated by roxygen2 (4.0.1): do not edit by hand
+\name{xgb.load}
+\alias{xgb.load}
+\title{Load xgboost model from binary file}
+\usage{
+xgb.load(modelfile)
+}
+\arguments{
+\item{modelfile}{the name of the binary file.}
+}
+\description{
+Load xgboost model from the binary model file
+}
+\examples{
+data(iris)
+bst <- xgboost(as.matrix(iris[,1:4]),as.numeric(iris[,5]), nrounds = 2)
+xgb.save(bst, 'iris.xgb.model')
+bst <- xgb.load('iris.xgb.model')
+pred <- predict(bst, as.matrix(iris[,1:4]))
+}
+
diff --git a/R-package/man/xgb.save.Rd b/R-package/man/xgb.save.Rd
new file mode 100644
index 000000000..ba390d1b4
--- /dev/null
+++ b/R-package/man/xgb.save.Rd
@@ -0,0 +1,23 @@
+% Generated by roxygen2 (4.0.1): do not edit by hand
+\name{xgb.save}
+\alias{xgb.save}
+\title{Save xgboost model to binary file}
+\usage{
+xgb.save(model, fname)
+}
+\arguments{
+\item{model}{the model object.}
+
+\item{fname}{the name of the binary file.}
+}
+\description{
+Save xgboost model from xgboost or xgb.train
+}
+\examples{
+data(iris)
+bst <- xgboost(as.matrix(iris[,1:4]),as.numeric(iris[,5]), nrounds = 2)
+xgb.save(bst, 'iris.xgb.model')
+bst <- xgb.load('iris.xgb.model')
+pred <- predict(bst, as.matrix(iris[,1:4]))
+}
+
diff --git a/R-package/man/xgb.train.Rd b/R-package/man/xgb.train.Rd
new file mode 100644
index 000000000..428ac9205
--- /dev/null
+++ b/R-package/man/xgb.train.Rd
@@ -0,0 +1,73 @@
+% Generated by roxygen2 (4.0.1): do not edit by hand
+\name{xgb.train}
+\alias{xgb.train}
+\title{eXtreme Gradient Boosting Training}
+\usage{
+xgb.train(params = list(), dtrain, nrounds, watchlist = list(),
+  obj = NULL, feval = NULL, ...)
+}
+\arguments{
+\item{params}{the list of parameters. Commonly used ones are:
+  objective: objective function, common ones are
+      - reg:linear  linear regression
+      - binary:logistic logistic regression for classification
+  eta: step size of each boosting step
+  max_depth: maximum depth of the tree
+  nthread: number of thread used in training, if not set, all threads are used
+
+  See \url{https://github.com/tqchen/xgboost/wiki/Parameters} for
+  further details. See also demo/demo.R for walkthrough example in R.}
+
+\item{dtrain}{takes an \code{xgb.DMatrix} as the input.}
+
+\item{nrounds}{the max number of iterations}
+
+\item{watchlist}{what information should be printed when \code{verbose=1} or
+  \code{verbose=2}. Watchlist is used to specify validation set monitoring
+  during training. For example user can specify
+   watchlist=list(validation1=mat1, validation2=mat2) to watch
+   the performance of each round's model on mat1 and mat2}
+
+\item{obj}{customized objective function. Given prediction and dtrain,
+return gradient and second order gradient.}
+
+\item{feval}{custimized evaluation function. Given prediction and dtrain,
+return a \code{list(metric='metric-name', value='metric-value')}.}
+
+\item{...}{other parameters to pass to \code{params}.}
+}
+\description{
+The training function of xgboost
+}
+\details{
+This is the training function for xgboost.
+
+Parallelization is automatically enabled if OpenMP is present.
+Number of threads can also be manually specified via "nthread" parameter.
+
+This function only accepts an \code{xgb.DMatrix} object as the input.
+It supports advanced features such as watchlist, customized objective function,
+therefore it is more flexible than \code{\link{xgboost}}.
+}
+\examples{
+data(iris)
+iris[,5] <- as.numeric(iris[,5])
+dtrain = xgb.DMatrix(as.matrix(iris[,1:4]), label=iris[,5])
+dtest = dtrain
+watchlist <- list(eval = dtest, train = dtrain)
+param <- list(max_depth = 2, eta = 1, silent = 1)
+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))
+}
+evalerror <- function(preds, dtrain) {
+  labels <- getinfo(dtrain, "label")
+  err <- as.numeric(sum(labels != (preds > 0)))/length(labels)
+  return(list(metric = "error", value = err))
+}
+bst <- xgb.train(param, dtrain, nround = 2, watchlist, logregobj, evalerror)
+}
+
diff --git a/R-package/man/xgboost.Rd b/R-package/man/xgboost.Rd
new file mode 100644
index 000000000..d9de7a421
--- /dev/null
+++ b/R-package/man/xgboost.Rd
@@ -0,0 +1,48 @@
+% Generated by roxygen2 (4.0.1): do not edit by hand
+\name{xgboost}
+\alias{xgboost}
+\title{eXtreme Gradient Boosting (Tree) library}
+\usage{
+xgboost(data = NULL, label = NULL, params = list(), nrounds,
+  verbose = 1, ...)
+}
+\arguments{
+\item{data}{takes \code{matrix}, \code{dgCMatrix}, local data file or
+\code{xgb.DMatrix}.}
+
+\item{label}{the response variable. User should not set this field,}
+
+\item{params}{the list of parameters. Commonly used ones are:
+  objective: objective function, common ones are
+      - reg:linear  linear regression
+      - binary:logistic logistic regression for classification
+  eta: step size of each boosting step
+  max_depth: maximum depth of the tree
+  nthread: number of thread used in training, if not set, all threads are used
+
+  See \url{https://github.com/tqchen/xgboost/wiki/Parameters} for
+  further details. See also demo/demo.R for walkthrough example in R.}
+
+\item{nrounds}{the max number of iterations}
+
+\item{verbose}{If 0, xgboost will stay silent. If 1, xgboost will print
+information of performance. If 2, xgboost will print information of both
+performance and construction progress information}
+
+\item{...}{other parameters to pass to \code{params}.}
+}
+\description{
+A simple interface for xgboost in R
+}
+\details{
+This is the modeling function for xgboost.
+
+Parallelization is automatically enabled if OpenMP is present.
+Number of threads can also be manually specified via "nthread" parameter
+}
+\examples{
+data(iris)
+bst <- xgboost(as.matrix(iris[,1:4]),as.numeric(iris[,5]), nrounds = 2)
+pred <- predict(bst, as.matrix(iris[,1:4]))
+}
+