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# CITATION.cff file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "PenalReg" in publications use:'
type: software
license: GPL-3.0-only
title: 'PenalReg: Automated Penalized Regression Analysis Using Ridge, Lasso and Elastic
  Net'
version: 0.1.0
doi: 10.32614/CRAN.package.PenalReg
abstract: 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)
  <https://doi.org/10.1080/00401706.1970.10488634>, Zou and Hastie (2005) <https://doi.org/10.1111/j.1467-9868.2005.00503.x>,
  and Friedman et al. (2010) <https://doi.org/10.18637/jss.v033.i01>.
authors:
- family-names: Shankar
  given-names: S. Vishnu
  email: S.vishnushankar55@gmail.com
- family-names: Lavanya
  given-names: V.
- family-names: Rathod
  given-names: Santosha
- family-names: Ray
  given-names: Mrinmoy
- family-names: Kumar
  given-names: Anil
repository: https://vishnumrstat.r-universe.dev
commit: eb17ff771d084100ddeae91d5b520f0191c8696c
date-released: '2026-08-24'
contact:
- family-names: Shankar
  given-names: S. Vishnu
  email: S.vishnushankar55@gmail.com