update links dmlc

R-package/DESCRIPTION

@@ -15,8 +15,8 @@ Description: Xgboost is short for eXtreme Gradient Boosting, which is an
     package is made to be extensible, so that users are also allowed to define
     their own objectives easily.
 License: Apache License (== 2.0) | file LICENSE
-URL: https://github.com/tqchen/xgboost
-BugReports: https://github.com/tqchen/xgboost/issues
+URL: https://github.com/dmlc/xgboost
+BugReports: https://github.com/dmlc/xgboost/issues
 VignetteBuilder: knitr
 Suggests: knitr
 Depends:

R-package/R/xgb.dump.R

@@ -11,9 +11,9 @@
 #' @param fname the name of the text file where to save the model text dump. If not provided or set to \code{NULL} the function will return the model as a \code{character} vector.
 #' @param fmap feature map file representing the type of feature. 
 #'        Detailed description could be found at 
-#'        \url{https://github.com/tqchen/xgboost/wiki/Binary-Classification#dump-model}.
+#'        \url{https://github.com/dmlc/xgboost/wiki/Binary-Classification#dump-model}.
 #'        See demo/ for walkthrough example in R, and
-#'        \url{https://github.com/tqchen/xgboost/blob/master/demo/data/featmap.txt} 
+#'        \url{https://github.com/dmlc/xgboost/blob/master/demo/data/featmap.txt} 
 #'        for example Format.
 #' @param with.stats whether dump statistics of splits 
 #'        When this option is on, the model dump comes with two additional statistics:

R-package/R/xgb.train.R

@@ -86,7 +86,7 @@
 #'      \item \code{ndcg} Normalized Discounted Cumulative Gain. \url{http://en.wikipedia.org/wiki/NDCG}
 #'   }
 #'   
-#' Full list of parameters is available in the Wiki \url{https://github.com/tqchen/xgboost/wiki/Parameters}.
+#' Full list of parameters is available in the Wiki \url{https://github.com/dmlc/xgboost/wiki/Parameters}.
 #' 
 #' This function only accepts an \code{\link{xgb.DMatrix}} object as the input.
 #' 

R-package/R/xgboost.R

@@ -20,7 +20,7 @@
 #'   \item \code{nthread} number of thread used in training, if not set, all threads are used
 #' }
 #'   
-#'   Look at \code{\link{xgb.train}} for a more complete list of parameters or \url{https://github.com/tqchen/xgboost/wiki/Parameters} for the full list.
+#'   Look at \code{\link{xgb.train}} for a more complete list of parameters or \url{https://github.com/dmlc/xgboost/wiki/Parameters} for the full list.
 #'   
 #'   See also \code{demo/} for walkthrough example in R.
 #' 

R-package/man/xgb.dump.Rd

@@ -13,9 +13,9 @@ xgb.dump(model = NULL, fname = NULL, fmap = "", with.stats = FALSE)
 
 \item{fmap}{feature map file representing the type of feature.
 Detailed description could be found at
-\url{https://github.com/tqchen/xgboost/wiki/Binary-Classification#dump-model}.
+\url{https://github.com/dmlc/xgboost/wiki/Binary-Classification#dump-model}.
 See demo/ for walkthrough example in R, and
-\url{https://github.com/tqchen/xgboost/blob/master/demo/data/featmap.txt}
+\url{https://github.com/dmlc/xgboost/blob/master/demo/data/featmap.txt}
 for example Format.}
 
 \item{with.stats}{whether dump statistics of splits

R-package/man/xgb.train.Rd

@@ -99,7 +99,7 @@ Number of threads can also be manually specified via \code{nthread} parameter.
      \item \code{ndcg} Normalized Discounted Cumulative Gain. \url{http://en.wikipedia.org/wiki/NDCG}
   }
 
-Full list of parameters is available in the Wiki \url{https://github.com/tqchen/xgboost/wiki/Parameters}.
+Full list of parameters is available in the Wiki \url{https://github.com/dmlc/xgboost/wiki/Parameters}.
 
 This function only accepts an \code{\link{xgb.DMatrix}} object as the input.
 }

R-package/man/xgboost.Rd

@@ -31,7 +31,7 @@ Commonly used ones are:
   \item \code{nthread} number of thread used in training, if not set, all threads are used
 }
 
-  Look at \code{\link{xgb.train}} for a more complete list of parameters or \url{https://github.com/tqchen/xgboost/wiki/Parameters} for the full list.
+  Look at \code{\link{xgb.train}} for a more complete list of parameters or \url{https://github.com/dmlc/xgboost/wiki/Parameters} for the full list.
 
   See also \code{demo/} for walkthrough example in R.}
 

R-package/vignettes/discoverYourData.Rmd

@@ -17,7 +17,7 @@ Introduction
 
 The purpose of this Vignette is to show you how to use **Xgboost** to discover and understand your own dataset better.
 
-This Vignette is not about predicting anything (see [Xgboost presentation](https://github.com/tqchen/xgboost/blob/master/R-package/vignettes/xgboostPresentation.Rmd)). We will explain how to use **Xgboost** to highlight the *link* between the *features* of your data and the *outcome*.
+This Vignette is not about predicting anything (see [Xgboost presentation](https://github.com/dmlc/xgboost/blob/master/R-package/vignettes/xgboostPresentation.Rmd)). We will explain how to use **Xgboost** to highlight the *link* between the *features* of your data and the *outcome*.
 
 Pacakge loading:
 
@@ -34,7 +34,7 @@ Preparation of the dataset
 ==========================
 
 Numeric VS categorical variables
-----------------------------------
+--------------------------------
 
 **Xgboost** manages only `numeric` vectors.
 
@@ -163,7 +163,7 @@ output_vector = df[,Improved] == "Marked"
 Build the model
 ===============
 
-The code below is very usual. For more information, you can look at the documentation of `xgboost` function (or at the vignette [Xgboost presentation](https://github.com/tqchen/xgboost/blob/master/R-package/vignettes/xgboostPresentation.Rmd)).
+The code below is very usual. For more information, you can look at the documentation of `xgboost` function (or at the vignette [Xgboost presentation](https://github.com/dmlc/xgboost/blob/master/R-package/vignettes/xgboostPresentation.Rmd)).
 
 ```{r}
 bst <- xgboost(data = sparse_matrix, label = output_vector, max.depth = 4,

R-package/vignettes/xgboostPresentation.Rmd

@@ -27,7 +27,7 @@ It is an efficient and scalable implementation of gradient boosting framework by
 
 It supports various objective functions, including *regression*, *classification* and *ranking*. The package is made to be extendible, so that users are also allowed to define their own objective functions easily. 
 
-It has been [used](https://github.com/tqchen/xgboost) to win several [Kaggle](http://www.kaggle.com) competitions. 
+It has been [used](https://github.com/dmlc/xgboost) to win several [Kaggle](http://www.kaggle.com) competitions. 
 
 It has several features:
 
@@ -49,7 +49,7 @@ Github version
 For up-to-date version (highly recommended), install from *Github*:
 
 ```{r installGithub, eval=FALSE}
-devtools::install_github('tqchen/xgboost', subdir='R-package')
+devtools::install_github('dmlc/xgboost', subdir='R-package')
 ```
 
 > *Windows* user will need to install [RTools](http://cran.r-project.org/bin/windows/Rtools/) first.