Package: logicDT 1.0.4

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>).

Authors:Michael Lau [aut, cre]

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logicDT/json (API)

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30 exports 2 stars 0.36 score 10 dependencies 2 scripts 347 downloads

Last updated 8 months agofrom:32a233a529. Checks:OK: 9. Indexed: yes.

Exports:bestBoostingItercalcAUCcalcBriercalcDevcalcMiscalcMSEcalcNCEcalcNRMSEcooling.schedulecv.prunefancy.plotfit4plModelfitLinearBoostingModelfitLinearLogicModelfitLinearModelget.ideal.penaltygetDesignMatrixgxe.testgxe.test.boostingimportance.test.boostinglogicDTlogicDT.bagginglogicDT.boostingpartial.predictpruneprune.pathrefitTreessplitSNPstree.controlvim

Dependencies:codetoolsforeachglmnetiteratorslatticeMatrixRcppRcppEigenshapesurvival