logicDT: Identifying Interactions Between Binary Predictors

A statistical learning method that tries to find the best set of predictors and interactions between predictors for modeling binary or quantitative response data in a decision tree. Several search algorithms and ensembling techniques are implemented allowing for finetuning the method to the specific problem. Interactions with quantitative covariables can be properly taken into account by fitting local regression models. Moreover, a variable importance measure for assessing marginal and interaction effects is provided. Implements the procedures proposed by Lau et al. (2024, <doi:10.1007/s10994-023-06488-6>).

Version:	1.0.5
Imports:	glmnet, graphics, stats, utils
Published:	2024-09-23
DOI:	10.32614/CRAN.package.logicDT
Author:	Michael Lau [aut, cre]
Maintainer:	Michael Lau <michael.lau at hhu.de>
License:	MIT + file LICENSE
NeedsCompilation:	yes
Materials:	README
CRAN checks:	logicDT results

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