aod, maptools, parallel, PBSmapping, raster, sp, terra, tools

c( person("Daniel", "Conn", role=c("aut", "cre"), email="djconn17@gmail.com"),
person("Tuck", "Ngun", role="aut"),
person(c("Christina M."), "Ramirez", role="aut"))
Fuzzy forests, a new algorithm based on random forests,
is designed to reduce the bias seen in random forest feature selection
caused by the presence of correlated features.  Fuzzy forests uses
recursive feature elimination random forests to select
features from separate blocks of correlated features where the
correlation within each block of features is high
and the correlation between blocks of features is low.
One final random forest is fit using the surviving features.
This package fits random forests using the 'randomForest' package and
allows for easy use of 'WGCNA' to split features into distinct blocks.
randomForest, foreach, doRNG, doParallel, parallel, ggplot2
Daniel Conn [aut, cre],
Tuck Ngun [aut],
Christina M. Ramirez [aut]

randomForest, foreach, doRNG, doParallel, parallel, ggplot2,
mvtnorm

Fuzzy forests, a new algorithm based on random forests,
is designed to reduce the bias seen in random forest feature selection
caused by the presence of correlated features.  Fuzzy forests uses
recursive feature elimination random forests to select
features from separate blocks of correlated features where the
correlation within each block of features is high
and the correlation between blocks of features is low.
One final random forest is fit using the surviving features.
This package fits random forests using the 'randomForest' package and
allows for easy use of 'WGCNA' to split features into distinct blocks.
See D. Conn, Ngun, T., C. Ramirez, and G. Li (2019) <doi:10.18637/jss.v091.i09>
for further details.

c(person(given = "Daniel",
family = "Conn",
role = c("aut", "cre"),
email = "djconn17@gmail.com"),
person(given = "Tuck",
family = "Ngun",
role = "aut"),
person(given = "Christina M.",
family = "Ramirez",
role = "aut"))
randomForest, foreach, doParallel, parallel, ggplot2, mvtnorm