Replace cBind by cbind (#3203)

* modify test_helper.R

* fix noLD

* update desc

* fix solaris test

* fix desc

* improve fix

* fix url

* change Matrix cBind to cbind

* fix

* fix error in demo

* fix examples

R-package/R/callbacks.R

@@ -554,6 +554,7 @@ cb.cv.predict <- function(save_models = FALSE) {
 #' #
 #' # In the iris dataset, it is hard to linearly separate Versicolor class from the rest
 #' # without considering the 2nd order interactions:
+#' require(magrittr)
 #' x <- model.matrix(Species ~ .^2, iris)[,-1]
 #' colnames(x)
 #' dtrain <- xgb.DMatrix(scale(x), label = 1*(iris$Species == "versicolor"))
@@ -602,7 +603,7 @@ cb.cv.predict <- function(save_models = FALSE) {
 #'
 #' # CV:
 #' bst <- xgb.cv(param, dtrain, nfold = 5, nrounds = 70, eta = 0.5,
-#'               callbacks = list(cb.gblinear.history(F)))
+#'               callbacks = list(cb.gblinear.history(FALSE)))
 #' # 1st forld of 1st class
 #' xgb.gblinear.history(bst, class_index = 0)[[1]] %>% matplot(type = 'l')
 #'
@@ -691,7 +692,9 @@ cb.gblinear.history <- function(sparse=FALSE) {
 #' corresponding to CV folds.
 #'
 #' @examples
+#' \dontrun{
 #' See \code{\link{cv.gblinear.history}}
+#' }
 #'
 #' @export
 xgb.gblinear.history <- function(model, class_index = NULL) {

R-package/R/xgb.create.features.R

@@ -83,5 +83,5 @@ xgb.create.features <- function(model, data, ...){
   check.deprecation(...)
   pred_with_leaf <- predict(model, data, predleaf = TRUE)
   cols <- lapply(as.data.frame(pred_with_leaf), factor)
-  cBind(data, sparse.model.matrix( ~ . -1, cols))
+  cbind(data, sparse.model.matrix( ~ . -1, cols))
 }

R-package/R/xgboost.R

@@ -77,7 +77,6 @@ NULL
 
 # Various imports
 #' @importClassesFrom Matrix dgCMatrix dgeMatrix
-#' @importFrom Matrix cBind
 #' @importFrom Matrix colSums
 #' @importFrom Matrix sparse.model.matrix
 #' @importFrom Matrix sparseVector