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: 2026-08-17 14:58:54 UTC; JARVIS
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>
Repository: CRAN
Date/Publication: 2026-08-24 14:40:07 UTC
