Package: glmdisc
Type: Package
Title: Discretization and Grouping for Logistic Regression
Version: 0.5
Date: 2020-02-20
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://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>).
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
Imports: caret (>= 6.0-82), 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: 7.0.2
Suggests: knitr, rmarkdown, testthat, covr
VignetteBuilder: knitr
Collate: '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'
NeedsCompilation: yes
Packaged: 2020-03-18 07:59:28 UTC; adimajo
Author: Adrien Ehrhardt [aut, cre],
  Vincent Vandewalle [aut],
  Christophe Biernacki [ctb],
  Philippe Heinrich [ctb]
Repository: CRAN
Date/Publication: 2020-03-22 11:30:02 UTC
Built: R 4.1.0; x86_64-w64-mingw32; 2020-08-03 14:26:54 UTC; windows
Archs: i386, x64
