glmdisc: Discretization and Grouping for Logistic Regression

A Stochastic-Expectation-Maximization (SEM) algorithm (Celeux et al. (1995) <https://hal.inria.fr/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) <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: 0.2
Imports: caret (≥ 6.0-82), gam, nnet, RcppNumerical, methods, MASS, graphics, Rcpp (≥ 0.12.13)
LinkingTo: Rcpp, RcppEigen, RcppNumerical
Suggests: knitr, rmarkdown, testthat, covr
Published: 2019-09-16
Author: Adrien Ehrhardt [aut, cre], Vincent Vandewalle [aut], Christophe Biernacki [ctb], Philippe Heinrich [ctb]
Maintainer: Adrien Ehrhardt <adrien.ehrhardt at inria.fr>
BugReports: https://github.com/adimajo/glmdisc/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://adimajo.github.io
NeedsCompilation: yes
CRAN checks: glmdisc results

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Reference manual: glmdisc.pdf
Vignettes: 'glmdisc' package
Package source: glmdisc_0.2.tar.gz
Windows binaries: r-devel: glmdisc_0.2.zip, r-release: glmdisc_0.2.zip, r-oldrel: glmdisc_0.2.zip
OS X binaries: r-release: glmdisc_0.2.tgz, r-oldrel: glmdisc_0.2.tgz
Old sources: glmdisc archive

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