diff --git a/R-package/R/xgb.create.features.R b/R-package/R/xgb.create.features.R
index f875b32fe..4365552e7 100644
--- a/R-package/R/xgb.create.features.R
+++ b/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:
 #' 
diff --git a/R-package/R/xgb.dump.R b/R-package/R/xgb.dump.R
index 9bd9a2135..86e97b4e7 100644
--- a/R-package/R/xgb.dump.R
+++ b/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
diff --git a/R-package/R/xgb.train.R b/R-package/R/xgb.train.R
index 20270605d..2ed2194d3 100644
--- a/R-package/R/xgb.train.R
+++ b/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."
diff --git a/R-package/man/xgb.create.features.Rd b/R-package/man/xgb.create.features.Rd
index 679203833..4f799444b 100644
--- a/R-package/man/xgb.create.features.Rd
+++ b/R-package/man/xgb.create.features.Rd
@@ -29,7 +29,7 @@ Joaquin Quinonero Candela)}
  
 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:
 
diff --git a/R-package/man/xgb.dump.Rd b/R-package/man/xgb.dump.Rd
index 206e32022..2ec26c743 100644
--- a/R-package/man/xgb.dump.Rd
+++ b/R-package/man/xgb.dump.Rd
@@ -24,9 +24,9 @@ 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.}
 
-\item{...}{currently not used}
+\item{dump_format}{either 'text' or 'json' format could be specified.}
 
-\item{dump_fomat}{either 'text' or 'json' format could be specified.}
+\item{...}{currently not used}
 }
 \value{
 if fname is not provided or set to \code{NULL} the function will return the model as a \code{character} vector. Otherwise it will return \code{TRUE}.
diff --git a/R-package/man/xgb.train.Rd b/R-package/man/xgb.train.Rd
index aea9c0a1b..4f37b78b8 100644
--- a/R-package/man/xgb.train.Rd
+++ b/R-package/man/xgb.train.Rd
@@ -174,7 +174,7 @@ The folloiwing is the list of built-in metrics for which Xgboost provides optimi
   \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."