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glmdisc  

Discretization and Grouping for Logistic Regression
View on CRAN: Click here


Download and install glmdisc package within the R console
Install from CRAN:
install.packages("glmdisc")

Install from Github:
library("remotes")
install_github("cran/glmdisc")

Install by package version:
library("remotes")
install_version("glmdisc", "0.6")



Attach the package and use:
library("glmdisc")
Maintained by
Adrien Ehrhardt
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-04-04
Latest Update:
Description:
A Stochastic-Expectation-Maximization SEM algorithm Celeux et al. 1995 httpshal.inria.frinria-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 arXiv1903.08920. 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 doi10.1093biomet57.1.97.
How to cite:
Adrien Ehrhardt (2019). glmdisc: Discretization and Grouping for Logistic Regression. R package version 0.6, https://cran.r-project.org/web/packages/glmdisc. Accessed 05 Aug. 2026.
Previous versions and publish date:
(2026-07-09 07:44), 0.1 (2019-04-04 14:10), 0.2 (2019-09-16 15:50), 0.3 (2019-12-16 14:50), 0.4 (2020-02-20 20:00), 0.5 (2020-03-22 12:30), 0.6 (2020-09-30 23:10)
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