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. For details, see Gramacy & Polson (2012 <doi:10.1214/12-BA719>).

Version: 1.2-7
Depends: R (≥ 2.14.0), methods, mvtnorm, boot, Matrix
Suggests: plgp
Published: 2023-04-25
DOI: 10.32614/CRAN.package.reglogit
Author: Robert B. Gramacy
Maintainer: Robert B. Gramacy <rbg at>
License: LGPL-2 | LGPL-2.1 | LGPL-3 [expanded from: LGPL]
NeedsCompilation: yes
Materials: ChangeLog
CRAN checks: reglogit results


Reference manual: reglogit.pdf


Package source: reglogit_1.2-7.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): reglogit_1.2-7.tgz, r-oldrel (arm64): reglogit_1.2-7.tgz, r-release (x86_64): reglogit_1.2-7.tgz, r-oldrel (x86_64): reglogit_1.2-7.tgz
Old sources: reglogit archive


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