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>).
| Version: | 0.1 |
| Imports: | caret (≥ 6.0-82), gam, nnet, RcppNumerical, methods, MASS, graphics, Rcpp (≥ 0.12.13) |
| LinkingTo: | Rcpp, RcppEigen, RcppNumerical |
| Suggests: | knitr, rmarkdown |
| Published: | 2019-04-04 |
| Author: | Adrien Ehrhardt [aut, cre], Vincent Vandewalle [aut], Christophe Biernacki [ctb], Philippe Heinrich [ctb] |
| Maintainer: | Adrien Ehrhardt <adrien.ehrhardt at inria.fr> |
| BugReports: | https://github.com/adimajo/glmdisc/issues |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://adimajo.github.io |
| NeedsCompilation: | yes |
| CRAN checks: | glmdisc results |
| Reference manual: | glmdisc.pdf |
| Vignettes: |
'glmdisc |
| Package source: | glmdisc_0.1.tar.gz |
| Windows binaries: | r-devel: glmdisc_0.1.zip, r-release: glmdisc_0.1.zip, r-oldrel: not available |
| OS X binaries: | r-release: glmdisc_0.1.tgz, r-oldrel: not available |
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