Package: PenalReg
Type: Package
Title: Automated Penalized Regression Analysis Using Ridge, Lasso and
        Elastic Net
Version: 0.1.0
Authors@R: c(person(given = "S. Vishnu", family = "Shankar", email = "S.vishnushankar55@gmail.com", role = c("aut", "cre")), person(given = "V.", family = "Lavanya", role = "aut"), person(given = "Santosha", family = "Rathod", role = "aut"), person(given = "Mrinmoy", family = "Ray", role = "aut"), person(given = "Anil", family = "Kumar", role = "aut"))
Description: 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>.
License: GPL-3
Encoding: UTF-8
Depends: R (>= 4.0.0)
Imports: caret, stats, utils
Suggests: glmnet
NeedsCompilation: no
Config/roxygen2/version: 8.1.0
Packaged: 2026-09-24 09:46:41 UTC; root
Author: S. Vishnu Shankar [aut, cre], V. Lavanya [aut], Santosha Rathod
        [aut], Mrinmoy Ray [aut], Anil Kumar [aut]
Maintainer: S. Vishnu Shankar <S.vishnushankar55@gmail.com>
Config/pak/sysreqs: libicu-dev
Repository: https://vishnumrstat.r-universe.dev
Date/Publication: 2026-08-24 14:40:07 UTC
RemoteUrl: https://github.com/cran/PenalReg
RemoteRef: HEAD
RemoteSha: eb17ff771d084100ddeae91d5b520f0191c8696c