Merge branch 'master' of ssh://github.com/dmlc/xgboost

R-package/R/xgb.plot.importance.R

@@ -54,4 +54,4 @@ xgb.plot.importance <- function(importance_matrix = NULL, numberOfClusters = c(1
 # Avoid error messages during CRAN check.
 # The reason is that these variables are never declared
 # They are mainly column names inferred by Data.table...
-globalVariables(c("Feature", "Gain", "Cluster"))
+globalVariables(c("Feature", "Gain", "Cluster", "ggplot", "aes", "geom_bar", "coord_flip", "xlab", "ylab", "ggtitle", "theme", "element_blank", "element_text"))

README.md

@@ -1,5 +1,6 @@
 XGBoost: eXtreme Gradient Boosting 
 ==================================
+
 An optimized general purpose gradient boosting library. The library is parallelized, and also provides an optimized distributed version.
 It implements machine learning algorithm under gradient boosting framework, including generalized linear model and gradient boosted regression tree (GBDT). XGBoost can also also distributed and scale to Terascale data
 
@@ -13,6 +14,8 @@ Please join [XGBoost User Group](https://groups.google.com/forum/#!forum/xgboost
   - Use issue tracker for bug reports, feature requests etc.
   - Use the user group to post your experience, ask questions about general usages.
 
+Gitter for developers [![Gitter chat for developers at https://gitter.im/dmlc/xgboost](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/dmlc/xgboost?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
+
 Distributed Version: [Distributed XGBoost](multi-node)
 
 Highlights of Usecases: [Highlight Links](doc/README.md#highlight-links)