Description: 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

Title: GLM model selection and multimodel inference made easy
Description: Automated model selection for GLMs. Provides a wrapper for
glm and similar functions, automatically generating all
possible models (under constraints set by the user) with the
specified response and explanatory variables, and finding the
best models in terms of some Information Criterion (AIC, AICc
or BIC). Can handle very large numbers of candidate models.
Features a Genetic Algorithm to find the best models when an
exhaustive screening of the candidates is not feasible.

Packaged: Mon Nov 30 22:29:47 2009; cvince

Packaged: Tue Dec 1 17:19:44 2009; cvince

Description: Automated model selection and model-averaging. Provides a
wrapper for glm and similar functions, automatically generating
all possible models (under constraints set by the user) with
the specified response and explanatory variables, and finding
the best models in terms of some Information Criterion (AIC,
AICc or BIC). Can handle very large numbers of candidate
models. Features a Genetic Algorithm to find the best models
when an exhaustive screening of the candidates is not feasible.

Suggests: MASS, aod, lme4, pscl, leaps, survival

Title: Model selection and multimodel inference made easy
Description: Automated model selection and model-averaging. Provides a wrapper for glm and similar functions, automatically generating all possible models (under constraints set by the user) with the specified response and explanatory variables, and finding the best models in terms of some Information Criterion (AIC, AICc or BIC).	Can handle very large numbers of candidate models.	Features a Genetic Algorithm to find the best models when an exhaustive screening of the candidates is not feasible.