PenalReg: Automated Penalized Regression Analysis Using Ridge, Lasso and
Elastic Net
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 (may not be active yet) |
| 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 |
Documentation:
Downloads:
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