kosel: Variable Selection by Revisited Knockoffs Procedures

Performs variable selection for many types of L1-regularised regressions using the revisited knockoffs procedure. This procedure uses a matrix of knockoffs of the covariates independent from the response variable Y. The idea is to determine if a covariate belongs to the model depending on whether it enters the model before or after its knockoff. The procedure suits for a wide range of regressions with various types of response variables. Regression models available are exported from the R packages 'glmnet' and 'ordinalNet'. Based on the paper linked to via the URL below: Gegout A., Gueudin A., Karmann C. (2019) <doi:10.48550/arXiv.1907.03153>.

Version: 0.0.1
Depends: R (≥ 1.1)
Imports: glmnet, ordinalNet
Suggests: graphics
Published: 2019-07-18
DOI: 10.32614/CRAN.package.kosel
Author: Clemence Karmann [aut, cre], Aurelie Gueudin [aut]
Maintainer: Clemence Karmann <clemence.karmann at gmail.com>
License: GPL-3
URL: https://arxiv.org/pdf/1907.03153.pdf
NeedsCompilation: no
CRAN checks: kosel results


Reference manual: kosel.pdf


Package source: kosel_0.0.1.tar.gz
Windows binaries: r-devel: kosel_0.0.1.zip, r-release: kosel_0.0.1.zip, r-oldrel: kosel_0.0.1.zip
macOS binaries: r-release (arm64): kosel_0.0.1.tgz, r-oldrel (arm64): kosel_0.0.1.tgz, r-release (x86_64): kosel_0.0.1.tgz, r-oldrel (x86_64): kosel_0.0.1.tgz


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