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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 vt.edu>
License: LGPL-2 | LGPL-2.1 | LGPL-3 [expanded from: LGPL]
URL: https://bobby.gramacy.com/r_packages/reglogit/
NeedsCompilation: yes
Materials: ChangeLog
CRAN checks: reglogit results

Documentation:

Reference manual: reglogit.pdf

Downloads:

Package source: reglogit_1.2-7.tar.gz
Windows binaries: r-devel: reglogit_1.2-7.zip, r-release: reglogit_1.2-7.zip, r-oldrel: reglogit_1.2-7.zip
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

Linking:

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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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