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It proposes a novel variable selection approach in classification problem that takes into account the correlations that may exist between the predictors of the design matrix in a high-dimensional logistic model. Our approach consists in rewriting the initial high-dimensional logistic model to remove the correlation between the predictors and in applying the generalized Lasso criterion.
| Version: | 2.1 |
| Depends: | R (≥ 3.5.0) |
| Imports: | cvCovEst, genlasso, tibble, MASS, ggplot2, Matrix, glmnet, corpcor |
| Suggests: | knitr |
| Published: | 2023-07-17 |
| DOI: | 10.32614/CRAN.package.WLogit |
| Author: | Wencan Zhu |
| Maintainer: | Wencan Zhu <wencan.zhu at yahoo.com> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| CRAN checks: | WLogit results |
| Reference manual: | WLogit.pdf |
| Vignettes: |
WLogit package |
| Package source: | WLogit_2.1.tar.gz |
| Windows binaries: | r-devel: WLogit_2.1.zip, r-release: WLogit_2.1.zip, r-oldrel: WLogit_2.1.zip |
| macOS binaries: | r-release (arm64): WLogit_2.1.tgz, r-oldrel (arm64): WLogit_2.1.tgz, r-release (x86_64): WLogit_2.1.tgz, r-oldrel (x86_64): WLogit_2.1.tgz |
| Old sources: | WLogit archive |
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