VSURF: Variable Selection Using Random Forests

Three steps variable selection procedure based on random forests. Initially developed to handle high dimensional data (for which number of variables largely exceeds number of observations), the package is very versatile and can treat most dimensions of data, for regression and supervised classification problems. First step is dedicated to eliminate irrelevant variables from the dataset. Second step aims to select all variables related to the response for interpretation purpose. Third step refines the selection by eliminating redundancy in the set of variables selected by the second step, for prediction purpose. Genuer, R. Poggi, J.-M. and Tuleau-Malot, C. (2015) <https://journal.r-project.org/archive/2015-2/genuer-poggi-tuleaumalot.pdf>.

Version: 1.2.0
Depends: R (≥ 4.2.0)
Imports: doParallel, foreach, parallel, randomForest, rpart
Suggests: testthat, ranger, Rborist
Published: 2022-12-15
DOI: 10.32614/CRAN.package.VSURF
Author: Robin Genuer [aut, cre], Jean-Michel Poggi [aut], Christine Tuleau-Malot [aut]
Maintainer: Robin Genuer <Robin.Genuer at u-bordeaux.fr>
BugReports: https://github.com/robingenuer/VSURF/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/robingenuer/VSURF
NeedsCompilation: no
Materials: NEWS
CRAN checks: VSURF results


Reference manual: VSURF.pdf


Package source: VSURF_1.2.0.tar.gz
Windows binaries: r-devel: VSURF_1.2.0.zip, r-release: VSURF_1.2.0.zip, r-oldrel: VSURF_1.2.0.zip
macOS binaries: r-release (arm64): VSURF_1.2.0.tgz, r-oldrel (arm64): VSURF_1.2.0.tgz, r-release (x86_64): VSURF_1.2.0.tgz, r-oldrel (x86_64): VSURF_1.2.0.tgz
Old sources: VSURF archive

Reverse dependencies:

Reverse imports: MSclassifR, SAiVE


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