lmridge: A Comprehensive R Package for Ridge Regression

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.

Muhammad Imdad Ullah , Muhammad Aslam , Saima Altaf

Supplementary materials

Supplementary materials are available in addition to this article. It can be downloaded at RJ-2018-060.zip

CRAN packages used

lmridge, ridge, MASS, lrmest, ltsbase, penalized, glmnet, RXshrink, rrBLUP, RidgeFusion, bigRR, lpridge, genridge, CoxRidge

CRAN Task Views implied by cited packages

MachineLearning, Survival, Distributions, Econometrics, Environmetrics, Multivariate, NumericalMathematics, Psychometrics, Robust, SocialSciences


Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".


For attribution, please cite this work as

Ullah, et al., "The R Journal: lmridge: A Comprehensive R Package for Ridge Regression", The R Journal, 2018

BibTeX citation

  author = {Ullah, Muhammad Imdad and Aslam, Muhammad and Altaf, Saima},
  title = {The R Journal: lmridge: A Comprehensive R Package for Ridge Regression},
  journal = {The R Journal},
  year = {2018},
  note = {https://doi.org/10.32614/RJ-2018-060},
  doi = {10.32614/RJ-2018-060},
  volume = {10},
  issue = {2},
  issn = {2073-4859},
  pages = {326-346}