Variable Selection in High-Dimensional Logistic Regression Models using a Whitening Approach
Overview
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.
Install
Health
NOTECRAN incoming feasibilityr-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc
Maintainer: ‘Wencan Zhu <wencan.zhu@yahoo.com>’
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Documentation
- Help pages
- 13includes a package overview page
- Examples that run
- 100%10 run in a plain check
- Exported functions with examples
- 75%
- Documented parameters
- not tracked
- Return-value docs
- not tracked
- Help pages citing references
- 15%
- WLogit package PDF
Downloads
Dependencies
Installing WLogit adds 89 packages (42 with compiled code, 82 MB of source tarballs). Heaviest: stringi 11 MB, ggplot2 6.0 MB, data.table 5.7 MB.
Code & Tests
Datasets
People & History
Maintainer since 1.0 (2022)
Pick two releases to compare their code metrics.
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- 2.1Latest
- unarchivedReturned to CRAN2023-06-08
- 2.02023-06-08 · diff ↗
- RR 4.3.0 released · 2023-04-21
- archivedRemoved from CRAN2022-08-12requires archived package 'genlasso'
- 1.02022-07-02
- RR 4.2.0 released · 2022-04-22
Package metadata
- First published
- 2022-07-02
- Total releases
- 3 / 4 yrs
- License
- GPL-2 OSI
- Minimum R
- ≥ 3.5.0
- Bundled data
- 381 KB / 4 files
- Download size
- 691 KB
Cite
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