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varPro: Model-Independent Variable Selection via the Rule-Based Variable Priority

A new framework of variable selection, which instead of generating artificial covariates such as permutation importance and knockoffs, creates release rules to examine the affect on the response for each covariate where the conditional distribution of the response variable can be arbitrary and unknown.

Version: 1.0.0
Depends: R (≥ 4.3.0)
Imports: randomForestSRC (≥ 3.4.5), glmnet, parallel, foreach, gbm, BART, umap, survival
Suggests: mlbench, doMC, caret, MASS, igraph
Published: 2025-12-11
DOI: 10.32614/CRAN.package.varPro (may not be active yet)
Author: Min Lu [aut], Aster K. Shear [aut], Udaya B. Kogalur [aut, cre], Hemant Ishwaran [aut]
Maintainer: Udaya B. Kogalur <ubk at kogalur.com>
BugReports: https://github.com/kogalur/varPro/issues/
License: GPL (≥ 3)
URL: https://www.varprotools.org/ https://www.luminwin.net/ https://ishwaran.org/
NeedsCompilation: yes
SystemRequirements: OpenMP
Citation: varPro citation info
Materials: NEWS
CRAN checks: varPro results

Documentation:

Reference manual: varPro.html , varPro.pdf

Downloads:

Package source: varPro_1.0.0.tar.gz
Windows binaries: r-devel: varPro_1.0.0.zip, r-release: not available, r-oldrel: varPro_1.0.0.zip
macOS binaries: r-release (arm64): varPro_1.0.0.tgz, r-oldrel (arm64): varPro_1.0.0.tgz, r-release (x86_64): varPro_1.0.0.tgz, r-oldrel (x86_64): varPro_1.0.0.tgz

Linking:

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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.