xgboost: Extreme Gradient Boosting

Extreme Gradient Boosting, which is an efficient implementation of gradient boosting framework. This package is its R interface. The package includes efficient linear model solver and tree learning algorithms. The package can automatically do parallel computation on a single machine which could be more than 10 times faster than existing gradient boosting packages. 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: 0.4-4
Depends: R (≥ 2.10)
Imports: Matrix (≥ 1.1-0), methods, data.table (≥ 1.9.4), magrittr (≥ 1.5), stringr (≥ 0.6.2)
Suggests: knitr, ggplot2 (≥ 1.0.0), DiagrammeR (≥ 0.6), Ckmeans.1d.dp (≥ 3.3.1), vcd (≥ 1.3)
Published: 2016-07-12
Author: Tianqi Chen, Tong He, Michael Benesty
Maintainer: Tong He <hetong007 at gmail.com>
BugReports: https://github.com/dmlc/xgboost/issues
License: Apache License (== 2.0) | file LICENSE
URL: https://github.com/dmlc/xgboost
NeedsCompilation: yes
CRAN checks: xgboost results

Downloads:

Reference manual: xgboost.pdf
Vignettes: Discover your data
Xgboost presentation
xgboost: eXtreme Gradient Boosting
Package source: xgboost_0.4-4.tar.gz
Windows binaries: r-devel: xgboost_0.4-4.zip, r-release: xgboost_0.4-4.zip, r-oldrel: xgboost_0.4-4.zip
OS X Mavericks binaries: r-release: xgboost_0.4-4.tgz, r-oldrel: xgboost_0.4-4.tgz
Old sources: xgboost archive

Reverse dependencies:

Reverse imports: SSL
Reverse suggests: FeatureHashing, GSIF, mlr, rBayesianOptimization