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>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Citation: varSelRF citation info
Materials: README
In views: ChemPhys, HighPerformanceComputing, MachineLearning
CRAN checks: varSelRF results


Reference manual: varSelRF.pdf


Package source: varSelRF_0.7-8.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
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


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