Provides an automated framework for penalized regression analysis using Ridge Regression, Lasso Regression and Elastic Net Regression. The package performs data standardization, training-testing data partitioning, cross-validation for hyperparameter tuning, model fitting, coefficient estimation, variable importance assessment, prediction, and performance evaluation. It simplifies regularized regression analysis by integrating the complete modeling workflow into a single function suitable for researchers for better understanding of the data.The methods are based on Hoerl and Kennard (1970) <doi:10.1080/00401706.1970.10488634>, Zou and Hastie (2005) <doi:10.1111/j.1467-9868.2005.00503.x>, and Friedman et al. (2010) <doi:10.18637/jss.v033.i01>.
| Version: | 0.1.0 |
| Depends: | R (≥ 4.0.0) |
| Imports: | caret, stats, utils |
| Suggests: | glmnet |
| Published: | 2026-08-24 |
| DOI: | 10.32614/CRAN.package.PenalReg |
| Author: | S. Vishnu Shankar [aut, cre], V. Lavanya [aut], Santosha Rathod [aut], Mrinmoy Ray [aut], Anil Kumar [aut] |
| Maintainer: | S. Vishnu Shankar <S.vishnushankar55 at gmail.com> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| Materials: | README, NEWS |
| CRAN checks: | PenalReg results |
| Reference manual: | PenalReg.html , PenalReg.pdf |
| Package source: | PenalReg_0.1.0.tar.gz |
| Windows binaries: | r-devel: PenalReg_0.1.0.zip, r-release: PenalReg_0.1.0.zip, r-oldrel: PenalReg_0.1.0.zip |
| macOS binaries: | r-release (arm64): PenalReg_0.1.0.tgz, r-oldrel (arm64): PenalReg_0.1.0.tgz, r-release (x86_64): PenalReg_0.1.0.tgz, r-oldrel (x86_64): PenalReg_0.1.0.tgz |
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