varSelRF: Variable selection using random forests

Variable selection from random forests using both backwards variable elimination (for the selection of small sets of non-redundant variables) and selection based on the importance spectrum (somewhat similar to scree plots; for the selection of large, potentially highly-correlated variables). Main applications in high-dimensional data (e.g., microarray data, and other genomics and proteomics applications). You can use rpvm instead of Rmpi if you want but I've only tested with Rmpi.

Version: 0.7-3
Depends: R (≥ 2.0.0), randomForest
Suggests: snow
Enhances: Rmpi
Published: 2010-10-28
Author: Ramon Diaz-Uriarte
Maintainer: Ramon Diaz-Uriarte <rdiaz02 at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
In views: ChemPhys, HighPerformanceComputing, MachineLearning
CRAN checks: varSelRF results


Reference manual: varSelRF.pdf
Package source: varSelRF_0.7-3.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X Snow Leopard binaries: r-release: varSelRF_0.7-3.tgz, r-oldrel: varSelRF_0.7-3.tgz
OS X Mavericks binaries: r-release: varSelRF_0.7-3.tgz
Old sources: varSelRF archive