Permutation Significance for Random Forests

Estimate False Discovery Rates (FDRs) for importance metrics from random forest runs.

Visually Exploring Random Forests

Graphic elements for exploring Random Forests using the 'randomForest' or 'randomForestSRC' package for survival, regression and classification forests and 'ggplot2' package plotting. Implements visualizations of the methods described in Breiman (2001) and Ishwaran, Kogalur, Blackstone, and Lauer (2008) .

Covariance Regression with Random Forests

Covariance Regression with Random Forests (CovRegRF) is a random forest method for estimating the covariance matrix of a multivariate response given a set of covariates. Random forest trees are built with a new splitting rule which is designed to maximize the distance between the sample covariance matrix estimates of the child nodes. The method is described in Alakus et al. (2023) . 'CovRegRF' uses 'randomForestSRC' package (Ishwaran and Kogalur, 2022) < https://cran.r-project.org/package=randomForestSRC> by freezing at the version 3.1.0. The custom splitting rule feature is utilised to apply the proposed splitting rule. The 'randomForestSRC' package implements 'OpenMP' by default, contingent upon the support provided by the target architecture and operating system. In this package, 'LAPACK' and 'BLAS' libraries are used for matrix decompositions.

Regression-Enhanced Random Forests

A novel generalized Random Forest method, that can improve on RFs by borrowing the strength of penalized parametric regression. Based on Zhang et al. (2019) .

Predictive Inference for Random Forests

An integrated package for constructing random forest prediction intervals using a fast implementation package 'ranger'. This package can apply the following three methods described in Haozhe Zhang, Joshua Zimmerman, Dan Nettleton, and Daniel J. Nordman (2019) : the out-of-bag prediction interval, the split conformal method, and the quantile regression forest.

Random Forests for Dependent Data

Fits non-linear regression models on dependant data with Generalised Least Square (GLS) based Random Forest (RF-GLS) detailed in Saha, Basu and Datta (2021) .

Prediction Intervals for Random Forests

Implements multiple state-of-the-art prediction interval methodologies for random forests. These include: quantile regression intervals, out-of-bag intervals, bag-of-observations intervals, one-step boosted random forest intervals, bias-corrected intervals, high-density intervals, and split-conformal intervals. The implementations include a combination of novel adjustments to the original random forest methodology and novel prediction interval methodologies. All of these methodologies can be utilized using solely this package, rather than a collection of separate packages. Currently, only regression trees are supported. Also capable of handling high dimensional data. Roy, Marie-Helene and Larocque, Denis (2019) . Ghosal, Indrayudh and Hooker, Giles (2018) . Zhu, Lin and Lu, Jiaxin and Chen, Yihong (2019) . Zhang, Haozhe and Zimmerman, Joshua and Nettleton, Dan and Nordman, Daniel J. (2019) . Meinshausen, Nicolai (2006) < http://www.jmlr.org/papers/volume7/meinshausen06a/meinshausen06a.pdf>. Romano, Yaniv and Patterson, Evan and Candes, Emmanuel (2019) . Tung, Nguyen Thanh and Huang, Joshua Zhexue and Nguyen, Thuy Thi and Khan, Imran (2014) .

Handwriting Analysis with Random Forests

Perform forensic handwriting analysis of two scanned handwritten documents. This package implements the statistical method described by Madeline Johnson and Danica Ommen (2021) . Similarity measures and a random forest produce a score-based likelihood ratio that quantifies the strength of the evidence in favor of the documents being written by the same writer or different writers.

Random Forest Cluster Analysis

Tools to perform random forest consensus clustering of different data types. The package is designed to accept a list of matrices from different assays, typically from high-throughput molecular profiling so that class discovery may be jointly performed. For references, please see Tao Shi & Steve Horvath (2006) & Monti et al (2003) .

Interactive Visualization Tool for Random Forests

An interactive data visualization and exploration toolkit that implements Breiman and Cutler's original random forest Java based visualization tools in R, for supervised and unsupervised classification and regression within the algorithm random forest.