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>.

Code for fitting and assessing models for the growth of trees. In 
    particular for the Bayesian neighborhood competition linear regression model 
    of Allen (2020): methods for model fitting and generating fitted/predicted 
    values, evaluating the effect of competitor species identity using 
    permutation tests, and evaluating model performance using spatial 
    cross-validation.