xgboost: eXtreme Gradient Boosting

This package is a R wrapper of xgboost, which is short for eXtreme Gradient Boosting. It is an efficient and scalable implementation of gradient boosting framework. The package includes efficient linear model solver and tree learning algorithms. The package can automatically do parallel computation with OpenMP, and it can be more than 10 times faster than existing gradient boosting packages such as gbm. It supports various objective functions, including regression, classification and ranking. The package is made to be extensible, so that users are also allowed to define their own objectives easily.

Version: 0.3-2
Depends: R (≥ 2.10)
Imports: Matrix (≥ 1.1-0), methods
Published: 2014-09-07
Author: Tianqi Chen, Tong He
Maintainer: Tong He <hetong007 at gmail.com>
BugReports: https://github.com/tqchen/xgboost/issues
License: Apache License (== 2.0) | file LICENSE
URL: https://github.com/tqchen/xgboost
NeedsCompilation: yes
Materials: README
CRAN checks: xgboost results


Reference manual: xgboost.pdf
Package source: xgboost_0.3-2.tar.gz
Windows binaries: r-devel: xgboost_0.3-2.zip, r-release: xgboost_0.3-2.zip, r-oldrel: xgboost_0.3-2.zip
OS X Snow Leopard binaries: r-release: xgboost_0.3-2.tgz, r-oldrel: xgboost_0.3-2.tgz
OS X Mavericks binaries: r-release: xgboost_0.3-2.tgz
Old sources: xgboost archive

Reverse dependencies:

Reverse suggests: mlr