glmdisc: Discretization and Grouping for Logistic Regression

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

Version: 0.1
Imports: caret (≥ 6.0-82), gam, nnet, RcppNumerical, methods, MASS, graphics, Rcpp (≥ 0.12.13)
LinkingTo: Rcpp, RcppEigen, RcppNumerical
Suggests: knitr, rmarkdown
Published: 2019-04-04
Author: Adrien Ehrhardt [aut, cre], Vincent Vandewalle [aut], Christophe Biernacki [ctb], Philippe Heinrich [ctb]
Maintainer: Adrien Ehrhardt <adrien.ehrhardt at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
CRAN checks: glmdisc results


Reference manual: glmdisc.pdf
Vignettes: 'glmdisc
Package source: glmdisc_0.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel: not available
OS X binaries: r-release: glmdisc_0.1.tgz, r-oldrel: not available


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