[R] Fix for cran submission of xgboost 0.6 (#1875)

fix cran check

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

@@ -18,7 +18,7 @@
 #'  
 #' International Workshop on Data Mining for Online Advertising (ADKDD) - August 24, 2014
 #' 
-#' \url{https://research.facebook.com/publications/758569837499391/practical-lessons-from-predicting-clicks-on-ads-at-facebook/}.
+#' \url{https://research.fb.com/publications/practical-lessons-from-predicting-clicks-on-ads-at-facebook/}.
 #' 
 #' Extract explaining the method:
 #' 

R-package/R/xgb.dump.R

@@ -14,7 +14,7 @@
 #'        When this option is on, the model dump comes with two additional statistics:
 #'        gain is the approximate loss function gain we get in each split;
 #'        cover is the sum of second order gradient in each node.
-#' @param dump_fomat either 'text' or 'json' format could be specified.
+#' @param dump_format either 'text' or 'json' format could be specified.
 #' @param ... currently not used
 #'
 #' @return

R-package/R/xgb.train.R

@@ -119,7 +119,7 @@
 #'   \itemize{
 #'      \item \code{rmse} root mean square error. \url{http://en.wikipedia.org/wiki/Root_mean_square_error}
 #'      \item \code{logloss} negative log-likelihood. \url{http://en.wikipedia.org/wiki/Log-likelihood}
-#'      \item \code{mlogloss} multiclass logloss. \url{https://www.kaggle.com/wiki/MultiClassLogLoss}
+#'      \item \code{mlogloss} multiclass logloss. \url{https://www.kaggle.com/wiki/MultiClassLogLoss/}
 #'      \item \code{error} Binary classification error rate. It is calculated as \code{(# wrong cases) / (# all cases)}.
 #'            By default, it uses the 0.5 threshold for predicted values to define negative and positive instances.
 #'            Different threshold (e.g., 0.) could be specified as "error@0."