reglogit: Simulation-Based Regularized Logistic Regression

Regularized (polychotomous) logistic regression by Gibbs sampling. The package implements subtly different MCMC schemes with varying efficiency depending on the data type (binary v. binomial, say) and the desired estimator (regularized maximum likelihood, or Bayesian maximum a posteriori/posterior mean, etc.) through a unified interface.

Getting started

Package details

AuthorRobert B. Gramacy <>
MaintainerRobert B. Gramacy <>
Package repositoryView on CRAN
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reglogit documentation built on May 2, 2019, 12:34 p.m.