glmdisc: Discretization and Grouping for Logistic Regression

A Stochastic-Expectation-Maximization (SEM) algorithm (Celeux et al. (1995) <https://inria.hal.science/inria-00074164>) associated with a Gibbs sampler which purpose is to learn a constrained representation for logistic regression that is called quantization (Ehrhardt et al. (2019) <doi:10.48550/arXiv.1903.08920>). Continuous features are discretized and categorical features' values are grouped to produce a better logistic regression model. Pairwise interactions between the discretized and grouped features are dynamically added to the model through a Metropolis-Hastings algorithm (Hastings, W. K. (1970) <doi:10.1093/biomet/57.1.97>).

Version: 0.8.1
Imports: gam, graphics, MASS, methods, Rcpp (≥ 1.0.7), RcppNumerical, stats
LinkingTo: Rcpp, RcppEigen, RcppNumerical
Suggests: covr, DT, knitr, nnet, rmarkdown, testthat (≥ 3.0.0)
Published: 2026-11-14
DOI: 10.32614/CRAN.package.glmdisc
Author: Adrien Ehrhardt ORCID iD [aut, cre], Vincent Vandewalle ORCID iD [aut], Christophe Biernacki ORCID iD [ctb], Philippe Heinrich [ctb]
Maintainer: Adrien Ehrhardt <adrien.ehrhardt at polytechnique.edu>
BugReports: https://github.com/adimajo/glmdisc/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://adimajo.github.io/glmdisc/, https://github.com/adimajo/glmdisc
NeedsCompilation: yes
Language: en-US
Citation: glmdisc citation info
Materials: NEWS
CRAN checks: glmdisc results

Documentation:

Reference manual: glmdisc.html , glmdisc.pdf
Vignettes: 'glmdisc' package (source, R code)

Downloads:

Package source: glmdisc_0.8.1.tar.gz
Windows binaries: r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries: r-release (arm64): not available, r-oldrel (arm64): not available, r-release (x86_64): not available, r-oldrel (x86_64): glmdisc_0.8.1.tgz
Old sources: glmdisc archive

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