wsrf: Weighted Subspace Random Forest for Classification

A parallel implementation of Weighted Subspace Random Forest. The Weighted Subspace Random Forest algorithm was proposed in the International Journal of Data Warehousing and Mining, 8(2):44-63, 2012, proposed by Baoxun Xu, Joshua Zhexue Huang, Graham Williams, Qiang Wang, and Yunming Ye. The algorithm can classify very high-dimensional data with random forests built using small subspaces. A novel variable weighting method is used for variable subspace selection in place of the traditional random variable sampling.This new approach is particularly useful in building models from high-dimensional data.

Version: 1.5.29
Depends: R (≥ 3.0.0), Rcpp (≥ 0.10.2), stats, parallel
LinkingTo: Rcpp
Suggests: rattle (≥ 2.6.26), randomForest (≥ 4.6.7), party (≥ 1.0.7), stringr (≥ 0.6.2), knitr (≥ 1.5)
Published: 2015-10-10
Author: Qinghan Meng [aut], He Zhao [aut, cre], Graham Williams [aut], Junchao Lv [ctb], Baoxun Xu [aut]
Maintainer: He Zhao <Simon.Yansen.Zhao at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: wsrf results


Reference manual: wsrf.pdf
Vignettes: A Quick Start Guide for wsrf
Package source: wsrf_1.5.29.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X Snow Leopard binaries: r-release: wsrf_1.5.24.tgz, r-oldrel: wsrf_1.4.0.tgz
OS X Mavericks binaries: r-release: wsrf_1.5.29.tgz
Old sources: wsrf archive