The R Journal: article published in 2018, volume 10:2

lmridge: A Comprehensive R Package for Ridge Regression
Muhammad Imdad Ullah, Muhammad Aslam and Saima Altaf , The R Journal (2018) 10:2, pages 326-346.

Abstract The ridge regression estimator, one of the commonly used alternatives to the conventional ordinary least squares estimator, avoids the adverse effects in the situations when there exists some considerable degree of multicollinearity among the regressors. There are many software packages available for estimation of ridge regression coefficients. However, most of them display limited methods to estimate the ridge biasing parameters without testing procedures. Our developed package, lmridge can be used to estimate ridge coefficients considering a range of different existing biasing parameters, to test these coefficients with more than 25 ridge related statistics, and to present different graphical displays of these statistics.

Received: 2018-03-02; online 2018-12-08, supplementary material, (1.6 KiB)
CRAN packages: lmridge, ridge, MASS, lrmest, ltsbase, penalized, glmnet, RXshrink, rrBLUP, RidgeFusion, bigRR, lpridge, genridge, CoxRidge
CRAN Task Views implied by cited CRAN packages: MachineLearning, Survival, Distributions, Econometrics, Environmetrics, Multivariate, NumericalMathematics, Psychometrics, Robust, SocialSciences

This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2018-060,
  author = {Muhammad Imdad Ullah and Muhammad Aslam and Saima Altaf},
  title = {{lmridge: A Comprehensive R Package for Ridge Regression}},
  year = {2018},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2018-060},
  url = {https://doi.org/10.32614/RJ-2018-060},
  pages = {326--346},
  volume = {10},
  number = {2}
}