resolving not-CRAN issues

R-package/DESCRIPTION

@@ -25,5 +25,4 @@ Imports:
     data.table (>= 1.9),
     magrittr (>= 1.5),
     stringr,
-    DiagrammeR,
-    vcd
+    DiagrammeR

R-package/R/predict.xgb.Booster.R

@@ -11,10 +11,10 @@ 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.
-#' @param predleaf whether predict leaf index instead. If set to TRUE, the output will be a matrix object.
 #' @param ntreelimit limit number of trees used in prediction, this parameter is
 #'  only valid for gbtree, but not for gblinear. set it to be value bigger 
 #'  than 0. It will use all trees by default.
+#' @param predleaf whether predict leaf index instead. If set to TRUE, the output will be a matrix object.
 #' @examples
 #' data(agaricus.train, package='xgboost')
 #' data(agaricus.test, package='xgboost')

R-package/R/xgb.cv.R

@@ -32,7 +32,7 @@
 #' @param nfold number of folds used
 #' @param label option field, when data is Matrix
 #' @param missing Missing is only used when input is dense matrix, pick a float
-#     value that represents missing value. Sometime a data use 0 or other extreme value to represents missing values.
+#'    value that represents missing value. Sometime a data use 0 or other extreme value to represents missing values.
 #' @param prediction A logical value indicating whether to return the prediction vector.
 #' @param showsd \code{boolean}, whether show standard deviation of cross validation
 #' @param metrics, list of evaluation metrics to be used in corss validation,

R-package/R/xgb.dump.R

@@ -29,7 +29,7 @@
 #' bst <- xgboost(data = train$data, label = train$label, max.depth = 2, 
 #'                eta = 1, nround = 2,objective = "binary:logistic")
 #' # save the model in file 'xgb.model.dump'
-#' xgb.dump(bst, 'xgb.model.dump', with.stats = T)
+#' xgb.dump(bst, 'xgb.model.dump', with.stats = TRUE)
 #' 
 #' # print the model without saving it to a file
 #' print(xgb.dump(bst))

R-package/R/xgb.importance.R

@@ -32,7 +32,8 @@
 #' data(agaricus.train, package='xgboost')
 #' data(agaricus.test, package='xgboost')
 #' 
-#' #Both dataset are list with two items, a sparse matrix and labels (labels = outcome column which will be learned). 
+#' #Both dataset are list with two items, a sparse matrix and labels 
+#'   (labels = outcome column which will be learned). 
 #' #Each column of the sparse Matrix is a feature in one hot encoding format.
 #' train <- agaricus.train
 #' test <- agaricus.test

R-package/R/xgb.model.dt.tree.R

@@ -42,7 +42,8 @@
 #' @examples
 #' data(agaricus.train, package='xgboost')
 #' 
-#' #Both dataset are list with two items, a sparse matrix and labels (labels = outcome column which will be learned). 
+#' #Both dataset are list with two items, a sparse matrix and labels 
+#'   (labels = outcome column which will be learned). 
 #' #Each column of the sparse Matrix is a feature in one hot encoding format.
 #' train <- agaricus.train
 #' 

R-package/R/xgb.plot.tree.R

@@ -42,7 +42,8 @@
 #' @examples
 #' data(agaricus.train, package='xgboost')
 #' 
-#' #Both dataset are list with two items, a sparse matrix and labels (labels = outcome column which will be learned). 
+#' #Both dataset are list with two items, a sparse matrix and labels 
+#'   (labels = outcome column which will be learned). 
 #' #Each column of the sparse Matrix is a feature in one hot encoding format.
 #' train <- agaricus.train
 #' 

R-package/demo/00Index

@@ -4,4 +4,5 @@ boost_from_prediction           Boosting from existing prediction
 predict_first_ntree             Predicting using first n trees
 generalized_linear_model        Generalized Linear Model
 cross_validation                Cross validation
-create_sparse_matrix
+create_sparse_matrix            Create Sparse Matrix
+predict_leaf_indices            Predicting the corresponding leaves

R-package/demo/create_sparse_matrix.R

@@ -1,7 +1,7 @@
 require(xgboost)
 require(Matrix)
 require(data.table)
-require(vcd) #Available in Cran. Used for its dataset with categorical values.
+if (!require(vcd)) install.packages('vcd') #Available in Cran. Used for its dataset with categorical values.
 
 # According to its documentation, Xgboost works only on numbers.
 # Sometimes the dataset we have to work on have categorical data.