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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 |
| 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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