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/>.
|Imports:||partykit, magrittr, methods, dplyr, caret|
|Suggests:||FactoMineR, knitr, testthat, covr, rmarkdown|
|Author:||Adrien Ehrhardt [aut, cre]|
|Maintainer:||Adrien Ehrhardt <adrien.ehrhardt at centraliens-lille.org>|
|License:||GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]|
|Citation:||glmtree citation info|
|CRAN checks:||glmtree results|
|Windows binaries:||r-devel: glmtree_0.2.zip, r-release: glmtree_0.2.zip, r-oldrel: glmtree_0.2.zip|
|macOS binaries:||r-release (arm64): glmtree_0.2.tgz, r-release (x86_64): glmtree_0.2.tgz, r-oldrel: glmtree_0.2.tgz|
|Old sources:||glmtree archive|
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