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.

Provides generic data structures and algorithms for use with forest
    mensuration data in a consistent framework. The functions and objects
    included are a collection of broadly applicable tools. More specialized 
    applications should be implemented in separate packages that build on this
    foundation. Documentation about 'ForestElementsR' is provided by three
    vignettes included in this package. For an introduction to the field of 
    forest mensuration, refer to the textbooks by Kershaw et al. (2017)
    <doi:10.1002/9781118902028>, and van Laar and Akca (2007)
    <doi:10.1007/978-1-4020-5991-9>.

Estimates the conditional error distributions of random forest
    predictions and common parameters of those distributions, including
    conditional misclassification rates, conditional mean squared prediction
    errors, conditional biases, and conditional quantiles, by out-of-bag
    weighting of out-of-bag prediction errors as proposed by Lu and Hardin
    (2021). This package is compatible with several existing packages that
    implement random forests in R.