Implementation of the Swiss Confederation's standard analysis
    model for salary analyses
    <https://www.ebg.admin.ch/en/equal-pay-analysis-with-logib> in R. The
    analysis is run at company-level and the model is intended for
    medium-sized and large companies. It can technically be used with 50
    or more employees (apprentices, trainees/interns and expats are not
    included in the analysis). Employees with at least 100 employees are
    required by the Gender Equality Act to conduct an equal pay analysis.
    This package allows users to run the equal salary analysis in R,
    providing additional transparency with respect to the methodology and
    simple automation possibilities.

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>).

Logic Forest is an ensemble machine learning method that identifies important and interpretable combinations of binary predictors using logic regression trees to model complex relationships with an outcome. Wolf, B.J., Slate, E.H., Hill, E.G. (2010) <doi:10.1093/bioinformatics/btq354>.