Adversarial Random Forests

Adversarial random forests (ARFs) recursively partition data into fully factorized leaves, where features are jointly independent. The procedure is iterative, with alternating rounds of generation and discrimination. Data becomes increasingly realistic at each round, until original and synthetic samples can no longer be reliably distinguished. This is useful for several unsupervised learning tasks, such as density estimation and data synthesis. Methods for both are implemented in this package. ARFs naturally handle unstructured data with mixed continuous and categorical covariates. They inherit many of the benefits of random forests, including speed, flexibility, and solid performance with default parameters. For details, see Watson et al. (2022) .

Ordered Random Forests

An implementation of the Ordered Forest estimator as developed in Lechner & Okasa (2019) . The Ordered Forest flexibly estimates the conditional probabilities of models with ordered categorical outcomes (so-called ordered choice models). Additionally to common machine learning algorithms the 'orf' package provides functions for estimating marginal effects as well as statistical inference thereof and thus provides similar output as in standard econometric models for ordered choice. The core forest algorithm relies on the fast C++ forest implementation from the 'ranger' package (Wright & Ziegler, 2017) .

Modified Ordered Random Forest

Nonparametric estimator of the ordered choice model using random forests. The estimator modifies a standard random forest splitting criterion to build a collection of forests, each estimating the conditional probability of a single class. The package also implements a nonparametric estimator of the covariates’ marginal effects.

Random Forests for Longitudinal Data

Random forests are a statistical learning method widely used in many areas of scientific research essentially for its ability to learn complex relationships between input and output variables and also its capacity to handle high-dimensional data. However, current random forests approaches are not flexible enough to handle longitudinal data. In this package, we propose a general approach of random forests for high-dimensional longitudinal data. It includes a flexible stochastic model which allows the covariance structure to vary over time. Furthermore, we introduce a new method which takes intra-individual covariance into consideration to build random forests. The method is fully detailled in Capitaine et.al. (2020) Random forests for high-dimensional longitudinal data.

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.

Accelerated Oblique Random Forests

Fit, interpret, and compute predictions with oblique random forests. Includes support for partial dependence, variable importance, passing customized functions for variable importance and identification of linear combinations of features. Methods for the oblique random survival forest are described in Jaeger et al., (2023) .

Permutation Significance for Random Forests

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

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.

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 (2020) .