Package: glmdisc 0.6

Adrien Ehrhardt

glmdisc: Discretization and Grouping for Logistic Regression

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>).

Authors:Adrien Ehrhardt [aut, cre], Vincent Vandewalle [aut], Christophe Biernacki [ctb], Philippe Heinrich [ctb]

glmdisc_0.6.tar.gz
glmdisc_0.6.zip(r-4.7-x86_64)glmdisc_0.6.zip(r-4.7-arm64)glmdisc_0.6.zip(r-4.6-x86_64)glmdisc_0.6.zip(r-4.6-arm64)
glmdisc_0.6.tar.gz(r-4.7-arm64)glmdisc_0.6.tar.gz(r-4.7-x86_64)glmdisc_0.6.tar.gz(r-4.6-arm64)glmdisc_0.6.tar.gz(r-4.6-x86_64)
glmdisc_0.6.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
glmdisc/json (API)

# Install 'glmdisc' in R:

install.packages('glmdisc', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/adimajo/glmdisc/issues

Uses libs:

c++– GNU Standard C++ Library v3

On CRAN:

cpp

2.46 score 29 scripts 52 downloads 8 exports 75 dependencies

Last updated from:d9ac9e4bd0. Checks:8 ERROR, 2 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	ERROR	194
linux-devel-x86_64	ERROR	241
source / vignettes	OK	302
linux-release-arm64	ERROR	179
linux-release-x86_64	ERROR	207
windows-devel-arm64	ERROR	341
windows-devel-x86_64	ERROR	315
windows-release-arm64	ERROR	316
windows-release-x86_64	ERROR	337
wasm-release	OK	121

Exports:contr.ltfr cutpoints discretize glmdisc normalizedGini plot predict predictlogisticRegression

Dependencies:caret class cli clock codetools cpp11 data.table diagram dplyr e1071 farver foreach future future.apply gam generics ggplot2 globals glue gower gtable hardhat ipred isoband iterators KernSmooth labeling lattice lava lifecycle listenv lubridate magrittr MASS Matrix ModelMetrics nlme nnet numDeriv parallelly pillar pkgconfig plyr pROC prodlim progressr proxy purrr R6 RColorBrewer Rcpp RcppEigen RcppNumerical recipes reshape2 rlang rpart S7 scales shape sparsevctrs SQUAREM stringi stringr survival tibble tidyr tidyselect timechange timeDate tzdb utf8 vctrs viridisLite withr

The glmdisc package: discretization at its finest

Rendered fromglmdisc.Rmdusingknitr::rmarkdown

Last update: 2020-09-30
Started: 2019-04-04

Help page	Topics
glmdisc: A package for discretizing continuous features, grouping categorical features' values and optimizing it for logistic regression.	glmdisc-package
Obtaining the cutpoints and / or regroupments of a discretization.	cutpoints cutpoints,glmdisc-method
Prediction on a raw test set of the best logistic regression model on discretized / grouped data.	discretize discretize,glmdisc-method
Model-based multivariate discretization for logistic regression.	glmdisc
Class glmdisc	glmdisc-class
Calculating the normalized Gini index	normalizedGini
Plots for the discretized / grouped data.	plot plot,glmdisc,ANY-method plot,glmdisc,missing-method plot.glmdisc
Prediction on a raw test set of the best logistic regression model on discretized data.	predict predict,glmdisc,ANY,ANY-method predict,glmdisc-method predict.glmdisc
Predicting using a logistic regression fitted with RCpp::fast_LR.	predictlogisticRegression

Package: glmdisc 0.6

glmdisc: Discretization and Grouping for Logistic Regression

Citation

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)