Package: glmtree 0.3.1

Adrien Ehrhardt

glmtree: Logistic Regression Trees

A logistic regression tree is a decision tree with logistic regressions at its leaves. A particular stochastic expectation maximization algorithm is used to draw a few good trees, that are then assessed via the user's criterion of choice among BIC / AIC / test set Gini. The formal development is given in a PhD chapter, see Ehrhardt (2019) <https://github.com/adimajo/manuscrit_these/releases/>.

Authors:Adrien Ehrhardt [aut, cre]

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glmtree.pdf |glmtree.html✨
glmtree/json (API)

# Install 'glmtree' in R:

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

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

On CRAN:

4.78 score 6 stars 3 scripts 182 downloads 2 mentions 3 exports 80 dependencies

Last updated 8 months agofrom:aaec00a7e1. Checks:OK: 7. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Oct 31 2024
R-4.5-win	OK	Oct 31 2024
R-4.5-linux	OK	Oct 31 2024
R-4.4-win	OK	Oct 31 2024
R-4.4-mac	OK	Oct 31 2024
R-4.3-win	OK	Oct 31 2024
R-4.3-mac	OK	Oct 31 2024

Exports:generateData glmtree normalizedGini

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

glmtree: logistic regression trees for efficient segmentation

Adrien Ehrhardt

Rendered fromglmtree.Rmdusingknitr::rmarkdownon Oct 31 2024.

Last update: 2024-03-05
Started: 2019-07-16

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Generates data from two logistic regression trees.	generateData
Class glmtree	glmdisc-class
Logistic regression tree by Stochastic-Expectation-Maximization	glmtree
Calculating the normalized Gini index	normalizedGini