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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:

Please use the canonical form https://CRAN.R-project.org/package=varSelRF to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.