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glmdisc
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]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-04-04
Latest Update: 2023-02-26
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
Previous versions and publish date:
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Functions, R codes and Examples using
the glmdisc R package
Some associated functions: contr.ltfr . cutpoints-method . discretize . glmdisc-class . glmdisc-package . glmdisc . normalizedGini . plot . predict . predictlogisticRegression .
Some associated R codes: 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 . Full glmdisc package functions and examples
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