Package: PenalReg 0.1.0

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>.

Authors:S. Vishnu Shankar [aut, cre], V. Lavanya [aut], Santosha Rathod [aut], Mrinmoy Ray [aut], Anil Kumar [aut]