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).

Version: 0.7-5
Depends: R (≥ 2.0.0), randomForest, parallel
Published: 2014-12-14
Author: Ramon Diaz-Uriarte
Maintainer: Ramon Diaz-Uriarte <rdiaz02 at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: http://ligarto.org/rdiaz/Software/Software.html, http://ligarto.org/rdiaz/Papers/rfVS/randomForestVarSel.html, https://github.com/rdiaz02/varSelRF
NeedsCompilation: no
Materials: README
In views: ChemPhys, HighPerformanceComputing, MachineLearning
CRAN checks: varSelRF results

Downloads:

Reference manual: varSelRF.pdf
Package source: varSelRF_0.7-5.tar.gz
Windows binaries: r-devel: varSelRF_0.7-5.zip, r-release: varSelRF_0.7-5.zip, r-oldrel: varSelRF_0.7-5.zip
OS X Snow Leopard binaries: r-release: varSelRF_0.7-5.tgz, r-oldrel: varSelRF_0.7-5.tgz
OS X Mavericks binaries: r-release: varSelRF_0.7-5.tgz
Old sources: varSelRF archive