DESCRIPTION

Package: glmdisc
Type: Package
Title: Discretization and Grouping for Logistic Regression
Version: 0.7.1
Date: 2024-03-05
Authors@R: 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")))
Maintainer: Adrien Ehrhardt <adrien.ehrhardt@centraliens-lille.org>
Description: A Stochastic-Expectation-Maximization (SEM) algorithm (Celeux et al. (1995) <https://inria.hal.science/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>).
License: GPL (>= 2)
Encoding: UTF-8
Imports:
    caret (>= 6.0-82),
    dplyr,
    magrittr,
    gam,
    nnet,
    RcppNumerical,
    methods,
    MASS,
    graphics,
    Rcpp (>= 0.12.13)
LinkingTo: Rcpp, RcppEigen, RcppNumerical
URL: https://adimajo.github.io
BugReports: https://github.com/adimajo/glmdisc/issues
RoxygenNote: 7.3.1
Suggests: 
    NPflow,
    Rfast,
    knitr,
    rmarkdown,
    testthat (>= 2.1.0),
    tibble,
    covr,
    DT
VignetteBuilder: knitr
Collate: 
    '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'