Fast binning of multiple variables using parallel processing. A summary of all the variables binned is generated which provides the information value, entropy, an indicator of whether the variable follows a monotonic trend or not, etc. It supports rebinning of variables to force a monotonic trend as well as manual binning based on pre specified cuts. The cut points of the bins are based on conditional inference trees as implemented in the partykit package. The conditional inference framework is described by Hothorn T, Hornik K, Zeileis A (2006) <doi:10.1198/106186006X133933>.

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>).

Two classification ensemble methods based on logic regression models.  LogForest() uses a bagging approach to construct an ensemble of logic regression models.  LBoost() uses a combination of boosting and cross-validation to construct an ensemble of logic regression models.  Both methods are used for classification of binary responses based on binary predictors and for identification of important variables and variable interactions predictive of a binary outcome. Wolf, B.J., Slate, E.H., Hill, E.G. (2010) <doi:10.1093/bioinformatics/btq354>.

Routines for fitting Logic Regression models. Logic Regression is described
	in Ruczinski, Kooperberg, and LeBlanc (2003) <DOI:10.1198/1061860032238>. Monte
        Carlo Logic Regression is described in and Kooperberg and Ruczinski (2005)
        <DOI:10.1002/gepi.20042>.