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) ). 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) ).


Reference manual

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0.1 by Adrien Ehrhardt, 2 months ago


Report a bug at https://github.com/adimajo/glmdisc/issues

Browse source code at https://github.com/cran/glmdisc

Authors: Adrien Ehrhardt [aut, cre] , Vincent Vandewalle [aut] , Christophe Biernacki [ctb] , Philippe Heinrich [ctb]

Documentation:   PDF Manual  

GPL (>= 2) license

Imports caret, gam, nnet, RcppNumerical, methods, MASS, graphics, Rcpp

Suggests knitr, rmarkdown

Linking to Rcpp, RcppEigen, RcppNumerical

See at CRAN