Discretization and Grouping for Logistic Regression
c(person("Adrien", "Ehrhardt", email = "adrien.ehrhardt@inria.fr",
role = c("aut", "cre")),
person("Vincent", "Vandewalle", email = "vincent.vandewalle@inria.fr",
role = c("aut")),
person("Christophe", "Biernacki", email = "christophe.biernacki@inria.fr",
role = c("ctb")),
person("Philippe", "Heinrich", email = "philippe.heinrich@univ-lille1.fr",
role = c("ctb")))
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>).
caret (>= 6.0-82), gam, nnet, RcppNumerical, methods, MASS,
graphics, Rcpp (>= 0.12.13)
'RcppExports.R' 'allClasses.R' 'cut.dataset.R'
'discretize.link.R' 'generic_cutpoints.R'
'generic_discretize.R' 'glmdisc.R' 'method_cutpoints.R'
'method_discretize.R' 'method_plot.R' 'method_predict.R'
'methods_disc.R' 'normalizedGini.R' 'semDiscretization.R'
Adrien Ehrhardt [aut, cre],
Vincent Vandewalle [aut],
Christophe Biernacki [ctb],
Philippe Heinrich [ctb]

c(person("Adrien", "Ehrhardt", email = "adrien.ehrhardt@centraliens-lille.org",
role = c("aut", "cre")),
person("Vincent", "Vandewalle", email = "vincent.vandewalle@inria.fr",
role = c("aut")),
person("Christophe", "Biernacki", email = "christophe.biernacki@inria.fr",
role = c("ctb")),
person("Philippe", "Heinrich", email = "philippe.heinrich@univ-lille1.fr",
role = c("ctb")))

caret (>= 6.0-82), dplyr, magrittr, gam, nnet, RcppNumerical,
methods, MASS, graphics, Rcpp (>= 0.12.13)
'RcppExports.R' 'allClasses.R' 'cut.dataset.R'
'discretize.link.R' 'generic_cutpoints.R'
'generic_discretize.R' 'glmdisc.R' 'initialization.R'
'method_cutpoints.R' 'method_discretize.R' 'method_plot.R'
'method_predict.R' 'methods_disc.R' 'normalizedGini.R'
'semDiscretization.R'