Robert B. Gramacy Professor of Statistics

Simulation-based regularized logistic regression

reglogit is an R package for regularized logistic regression by Gibbs sampling. Ssubtly different MCMC schemes are tailored to the data type (binary v. binomial, say) and the desired estimator (regularized MLE, or Bayesian maximum a posteriori/posterior mean, etc.) through a unified interface.

This software is licensed under the GNU Lesser Public License (LGPL), version 2 or later. See the change log and an archive of previous versions.

The package provides:

  • logistic regression under lasso and ridge penalties via a coupled pair of data augmentation schemes
  • power-posterior analysis for calculating MAP estimators
  • Binomial logistic regression without data expansion (i.e., via a binarization)

New! The latest version supports:

  • polychotomous (3+) outputs, i.e., multinomial logistic regression, through regmlogit
  • faster computation for sparse design matrices

Obtaining the package

  • Download R from by selecting the version for your operating system.
  • Install the reglogit, mvtnorm and boot packages, from within R.
    R> install.packages(c("reglogit", "mvtnorm", "boot"))
  • Optionally, install the Matrix package.
    R> install.packages("Matrix")
  • Load the library as you would for any R library.
    R> library(reglogit)


  • See the package documentation. A pdf version of the reference manual, or help pages, is also available. The help pages can be accessed from within R. Try starting with:
    R> help(package=reglogit)
    R> ?reglogit    # follow the examples
  • For details on the use of this software for estimating player abilities in hockey, please see the paper linked below and our Chicago Hockey Analytics page.