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 |
Documentation:
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