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.

A small subset of plots throughout the U.S. are sampled and
    assessed "on-the-ground" as forested or non-forested by the U.S.
    Department of Agriculture, Forest Service, Forest Inventory and
    Analysis (FIA) Program, but the FIA also has access to remotely sensed
    data for all land in the country. The 'forested' package contains data
    frames intended for use in predictive modeling applications where the more
    easily-accessible remotely sensed data can be used to predict whether a plot 
    is forested or non-forested. Currently, the package provides data for
    Washington and Georgia.