Estimates average treatment effects using kernel energy balancing
    with random forest similarity kernels. A multivariate random forest jointly
    models covariates, outcome, and treatment to build a similarity kernel
    between observations. This kernel is then used for energy balancing to create
    weights that control for confounding. The method is described in
    De and Huling (2025) <doi:10.48550/arXiv.2512.18069>.

Approximate false positive rate control in selection frequency for
    random forest using the methods described by Ender Konukoglu and Melanie Ganz (2014) <arXiv:1410.2838>.
    Methods for calculating the selection frequency threshold at false positive rates
    and selection frequency false positive rate feature selection.

Supervised, multivariate, and non-parametric discretization algorithm based on tree ensembles learning and moment matching optimization. This version of the algorithm relies on random forest algorithm to learn a large set of split points that conserves the relationship between attributes and the target class, and on moment matching optimization to transform this set into a reduced number of cut points matching as well as possible statistical properties of the initial set of split points. For each attribute to be discretized, the set S of its related split points extracted through random forest is mapped to a reduced set C of cut points of size k. This mapping relies on minimizing, for each continuous attribute to be discretized, the distance between the four first moments of S and the four first moments of C subject to some constraints. This non-linear optimization problem is performed using k values ranging from 2 to 'max_splits', and the best solution returned correspond to the value k which optimum solution is the lowest one over the different realizations. ForestDisc is a generalization of RFDisc discretization method initially proposed by Berrado and Runger (2009) <doi:10.1109/AICCSA.2009.5069327>, and improved by Berrado et al. in 2012 by adopting the idea of moment matching optimization related by Hoyland and Wallace (2001) <doi: 10.1287/mnsc.47.2.295.9834>.