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 [aut, cre], Vincent Vandewalle [aut], Christophe Biernacki [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): glmdisc_0.8.1.tgz, r-oldrel (arm64): glmdisc_0.8.1.tgz, r-release (x86_64): glmdisc_0.8.1.tgz, r-oldrel (x86_64): glmdisc_0.8.1.tgz
Old sources:	glmdisc archive

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