resolving not-CRAN issues

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))
@@ -54,4 +54,4 @@ xgb.dump <- function(model = NULL, fname = NULL, fmap = "", with.stats=FALSE) {
     result %>% str_split("\n") %>% unlist %>% Filter(function(x) x != "", .) %>% writeLines(fname)
     return(TRUE)
   }
-}
+}

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
 #'