glmdisc (0.2)

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) ). 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) ).

Maintainer: Adrien Ehrhardt
Author(s): Adrien Ehrhardt [aut, cre], Vincent Vandewalle [aut], Christophe Biernacki [ctb], Philippe Heinrich [ctb]

License: GPL (>= 2)

Uses: caret, gam, MASS, nnet, Rcpp, RcppNumerical, testthat, knitr, rmarkdown, covr

Released about 1 month ago.