Metrics and statistics of Essential Biodiversity Variables are computed by processing Global Forest Change data (Hansen et al., 2013) <doi:10.1126/science.1244693> , Canopy Cover data (Sexton et al., 2013) <doi:10.1080/17538947.2013.786146>, and polygon geometries (e.g., GADM, see <https://gadm.org/>).

Approximate False Positive Rate Control in Selection Frequency
for Random Forest
Approximate false positive rate control in selection frequency for
random forest using the methods described by Ender Konukoglu and Melanie Ganz (2015) <arXiv:1410.2838>.
Methods for calculating the selection frequency threshold at false positive rates
and selection frequency false positive rate feature selection.

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.

c(
person("Tom", "Wilson", , "tpw2@aber.ac.uk", c("aut", "cre"), comment = c(ORCID = "0000-0003-3112-4682")),
person("Jasen", "Finch", , "jsf9@aber.ac.uk", "aut"))

testthat, randomForest, ranger, parsnip, knitr, rmarkdown

A Unified Framework for Random Forest Prediction Error
Estimation
Estimates the conditional error distributions of random forest
predictions and common parameters of those distributions, including
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 (2019+) <arXiv:1912.07435>.
This package is compatible with several existing packages that
implement random forests in R.

Estimates the conditional error distributions of random forest
predictions and common parameters of those distributions, including
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.