abind, dplyr, ggplot2, grDevices, gridExtra, magrittr,
methods, parallel, randomForest, rlang, plyr, scales, stats,
swfscMisc (>= 1.5), tibble, tidyr, utils

Random Forests Model Selection and Performance Evaluation
c(
person("Jeffrey S.", "Evans", role = c("cre", "aut"),  email = "jeffrey_evans@tnc.org"),
person("Melanie A.", "Murphy", role = "ctb") )
Utilities for Random Forest model selection, class balance
correction, significance test, cross validation and partial dependency
plots.
Jeffrey S. Evans [cre, aut],
Melanie A. Murphy [ctb]

Random Forests model selection and performance evaluation
as.person(c(person(family="Evans", given="Jeffrey S.",
email="jeffrey_evans@tnc.org", role = c("aut", "cre")),
person(family="Murphy", given="Melanie A.", role = c("aut"))))
Utilities for Random Forest model selection, class balance
correction, significance test, cross validation and probability partial
plots.
Jeffrey S. Evans [aut, cre],
Melanie A. Murphy [aut]

Unbiased Variable Importance for Random Forests
Computes a novel variable importance for random forests: Impurity reduction importance scores for out-of-bag (OOB) data complementing the existing inbag Gini importance, see also <doi: 10.1080/03610926.2020.1764042>.
The Gini impurities for inbag and OOB data are combined in three different ways, after which the information gain is computed at each split.
This gain is aggregated for each split variable in a tree and averaged across trees.

Computes a novel variable importance for random forests: Impurity reduction importance scores for out-of-bag (OOB) data complementing the existing inbag Gini importance, see also Strobl et al (2007) <doi:10.1186/1471-2105-8-25>, Strobl et al (2007) <doi:10.1016/j.csda.2006.12.030> and Breiman (2001) <DOI:10.1023/A:1010933404324>.
The Gini impurities for inbag and OOB data are combined in three different ways, after which the information gain is computed at each split.
This gain is aggregated for each split variable in a tree and averaged across trees.