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.

Random Forest with Multivariate Longitudinal Predictors

Based on random forest principle, 'DynForest' is able to include multiple longitudinal predictors to provide individual predictions. Longitudinal predictors are modeled through the random forest. The methodology is fully described for a survival outcome in: Devaux, Helmer, Genuer & Proust-Lima (2023) .

Weighted Subspace Random Forest for Classification

A parallel implementation of Weighted Subspace Random Forest. The Weighted Subspace Random Forest algorithm was proposed in the International Journal of Data Warehousing and Mining by Baoxun Xu, Joshua Zhexue Huang, Graham Williams, Qiang Wang, and Yunming Ye (2012) . The algorithm can classify very high-dimensional data with random forests built using small subspaces. A novel variable weighting method is used for variable subspace selection in place of the traditional random variable sampling.This new approach is particularly useful in building models from high-dimensional data.

Tune Random Forest of the 'ranger' Package

Tuning random forest with one line. The package is mainly based on the packages 'ranger' and 'mlrMBO'.

An Implementation of the Hedged Random Forest Algorithm

This algorithm is described in detail in the paper "Hedging Forecast Combinations With an Application to the Random Forest" by Beck et al. (2023) . The package provides a function hedgedrf() that can be used to train a Hedged Random Forest model on a dataset, and a function predict.hedgedrf() that can be used to make predictions with the model.

Random Forest Two-Sample Tests

An implementation of Random Forest-based two-sample tests as introduced in Hediger & Michel & Naef (2022).

Find the Outlier by Quantile Random Forests

Provides a method to find the outlier in custom data by quantile random forests method. Introduced by Meinshausen Nicolai (2006) < https://dl.acm.org/doi/10.5555/1248547.1248582>. It directly calls the ranger() function of the 'ranger' package to perform data fitting and prediction. We also implement the evaluation of outlier prediction results. Compared with random forest detection of outliers, this method has higher accuracy and stability on large datasets.