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.

Visualizes Random Forests with Feature Contributions
Form visualizations of high dimensional mapping structures of random forests and feature contributions.
Rcpp (>= 0.11.3), rgl, kknn, randomForest

Enables user to form appropriate visualization of high dimensional mapping curvature of random forests.
Rcpp (>= 0.11.6), randomForest, rgl, kknn, trimTrees

Enables users to form appropriate visualizations of high dimensional mapping curvatures of random forests.

Rcpp (>= 0.11.3), rgl, kknn, ggplot2, gridExtra

Enables users to form appropriate visualizations of high dimensional mapping structures of random forests.

Forest Health Evaluation System at the Forest Stand Level in
China
Assessing forest ecosystem health is an effective way for forest
resource management.The national forest health evaluation system at the forest
stand level using analytic hierarchy process, has a high application value
and practical significance. The package can effectively and easily realize the
total assessment process, and help foresters to further assess and management
forest resources.

Implements the Forest-R.K. Algorithm for Classification Problems
Provides functions that calculates common types of splitting
criteria used in random forests for classification problems, as well as
functions that make predictions based on a single tree or a Forest-R.K. model;
the package also provides functions to generate importance plot for a
Forest-R.K. model, as well as the 2D multidimensional-scaling plot of
data points that are colour coded by their predicted class types by the
Forest-R.K. model. This package is based on:
Bernard, S., Heutte, L., Adam, S., (2008, ISBN:978-3-540-85983-3)
"Forest-R.K.: A New Random Forest Induction Method",
Fourth International Conference on Intelligent Computing,
September 2008, Shanghai, China, pp.430-437.
c(
person("Hyunjin", "Cho", email = "h56cho@uwaterloo.ca", role = c("aut","cre")),
person("Rebecca", "Su", email = "y57su@uwaterloo.ca", role = "ctb"))
Hyunjin Cho [aut, cre],
Rebecca Su [ctb]
igraph, ggplot2, rapportools, partykit, stats, graphics,
pkgKitten, knitr, mlbench
The package is also based on the discussion
https://stats.stackexchange.com/questions/168964/building-a-regression-tree-with-r-from-scratch/168967#168967