wsrf: Weighted Subspace Random Forest

The wsrf package is a parallel implementation of the Weighted Subspace Random Forest algorithm proposed (wsrf). A novel variable weighting method is used for variable subspace selection in place of the traditional approach of random variable sampling. This new approach is particularly useful in building models for high dimensional data—often consisting of thousands of variables. Parallel computation is used to take advantage of multi-core machines and clusters of machines to build random forest models from high dimensional data with reduced elapsed times.

Version: 1.4.0
Depends: R (≥ 3.0.0), Rcpp (≥ 0.10.2), parallel
LinkingTo: Rcpp
Suggests: rattle (≥ 2.6.26), randomForest (≥ 4.6.7), party (≥ 1.0.7), stringr (≥ 0.6.2), knitr (≥ 1.5)
Published: 2014-05-30
Author: Qinghan Meng [aut], He Zhao [aut, cre], Graham Williams [aut], Junchao Lv [ctb]
Maintainer: He Zhao <Simon.Yansen.Zhao at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: wsrf results

Downloads:

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