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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