Models Multivariate Cases Using Random Forests

Models and predicts multiple output features in single random forest considering the linear relation among the output features, see details in Rahman et al (2017).

Extensible, Parallelizable Implementation of the Random Forest Algorithm

Scalable implementation of classification and regression forests, as described by Breiman (2001), .

Autoencoding Random Forests

Autoencoding Random Forests ('RFAE') provide a method to autoencode mixed-type tabular data using Random Forests ('RF'), which involves projecting the data to a latent feature space of user-chosen dimensionality (usually a lower dimension), and then decoding the latent representations back into the input space. The encoding stage is useful for feature engineering and data visualisation tasks, akin to how principal component analysis ('PCA') is used, and the decoding stage is useful for compression and denoising tasks. At its core, 'RFAE' is a post-processing pipeline on a trained random forest model. This means that it can accept any trained RF of 'ranger' object type: 'RF', 'URF' or 'ARF'. Because of this, it inherits Random Forests' robust performance and capacity to seamlessly handle mixed-type tabular data. For more details, see Vu et al. (2025) .

Distributional Random Forests

An implementation of distributional random forests as introduced in Cevid & Michel & Naf & Meinshausen & Buhlmann (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) .

"Dirichlet Random Forest"

Implementation of the Dirichlet Random Forest algorithm for compositional response data. Supports maximum likelihood estimation ('MLE') and method-of-moments ('MOM') parameter estimation for the Dirichlet distribution. Provides two prediction strategies; averaging-based predictions (average of responses within terminal nodes) and parameter-based predictions (expected value derived from the estimated Dirichlet parameters within terminal nodes). For more details see Masoumifard, van der Westhuizen, and Gardner-Lubbe (2026, ISBN:9781032903910).

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.

Permutation Significance for Random Forests

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