Automated Penalized Regression Analysis Using Ridge, Lasso and Elastic Net R package PenalReg version 0.1.0 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>. Software Depends: R (>= 4.0.0) Imports: caret, stats, utils Suggests: glmnet S. Vishnu Shankar <S.vishnushankar55@gmail.com> Comprehensive R Archive Network (CRAN) S. Vishnu Shankar [aut, cre], V. Lavanya [aut], Santosha Rathod [aut], Mrinmoy Ray [aut], Anil Kumar [aut] GPL-3 2026-08-24 application/tgz https://CRAN.R-project.org/package=PenalReg doi:10.32614/CRAN.package.PenalReg