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: 0.7-8
Depends: R (≥ 2.0.0), randomForest, parallel
Published: 2017-07-10
DOI: 10.32614/CRAN.package.varSelRF
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
Citation: varSelRF citation info
Materials: README
In views: ChemPhys, HighPerformanceComputing, MachineLearning
CRAN checks: varSelRF results

Documentation:

Reference manual: varSelRF.pdf

Downloads:

Package source: varSelRF_0.7-8.tar.gz
Windows binaries: r-devel: varSelRF_0.7-8.zip, r-release: varSelRF_0.7-8.zip, r-oldrel: varSelRF_0.7-8.zip
macOS binaries: r-release (arm64): varSelRF_0.7-8.tgz, r-oldrel (arm64): varSelRF_0.7-8.tgz, r-release (x86_64): varSelRF_0.7-8.tgz, r-oldrel (x86_64): varSelRF_0.7-8.tgz
Old sources: varSelRF archive

Reverse dependencies:

Reverse imports: a4Classif
Reverse suggests: varrank

Linking:

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