Prediction Intervals with Random Forests and Boosted Forests

Implements various prediction interval methods with random forests and boosted forests. The package has two main functions: pibf() produces prediction intervals with boosted forests (PIBF) as described in Alakus et al. (2022) and rfpi() builds 15 distinct variations of prediction intervals with random forests (RFPI) proposed by Roy and Larocque (2020) .

Binomial Random Forest Feature Selection

The 'binomialRF' is a new feature selection technique for decision trees that aims at providing an alternative approach to identify significant feature subsets using binomial distributional assumptions (Rachid Zaim, S., et al. (2019)) . Treating each splitting variable selection as a set of exchangeable correlated Bernoulli trials, 'binomialRF' then tests whether a feature is selected more often than by random chance.

Multiple Imputation using Chained Random Forests

An R package for multiple imputation using chained random forests. Implemented methods can handle missing data in mixed types of variables by using prediction-based or node-based conditional distributions constructed using random forests. For prediction-based imputation, the method based on the empirical distribution of out-of-bag prediction errors of random forests and the method based on normality assumption for prediction errors of random forests are provided for imputing continuous variables. And the method based on predicted probabilities is provided for imputing categorical variables. For node-based imputation, the method based on the conditional distribution formed by the predicting nodes of random forests, and the method based on proximity measures of random forests are provided. More details of the statistical methods can be found in Hong et al. (2020) .

Causal Effect Random Forest of Interaction Trees

Fits a Causal Effect Random Forest of Interaction Tress (CERFIT) which is a modification of the Random Forest algorithm where each split is chosen to maximize subgroup treatment heterogeneity. Doing this allows it to estimate the individualized treatment effect for each observation in either randomized controlled trial (RCT) or observational data. For more information see L. Li, R. A. Levine, and J. Fan (2022) .

Random Forests Model Selection and Performance Evaluation

Utilities for Random Forest model selection, class balance correction, significance test, cross validation and partial dependency plots.

Movement to Behaviour Inference using Random Forest

Prediction of behaviour from movement characteristics using observation and random forest for the analyses of movement data in ecology. From movement information (speed, bearing...) the model predicts the observed behaviour (movement, foraging...) using random forest. The model can then extrapolate behavioural information to movement data without direct observation of behaviours. The specificity of this method relies on the derivation of multiple predictor variables from the movement data over a range of temporal windows. This procedure allows to capture as much information as possible on the changes and variations of movement and ensures the use of the random forest algorithm to its best capacity. The method is very generic, applicable to any set of data providing movement data together with observation of behaviour.

Improving Interaction Modelling and Interpretability in Random Forests

Implementation of the unity forest (UFO) framework (Hornung & Hapfelmeier, 2026, ). UFOs are a random forest variant designed to better take covariates with purely interaction-based effects into account, including interactions for which none of the involved covariates exhibits a marginal effect. While this framework tends to improve discrimination and predictive accuracy compared to standard random forests, it also facilitates the identification and interpretation of (marginal or interactive) effects: In addition to the UFO algorithm for tree construction, the package includes the unity variable importance measure (unity VIM), which quantifies covariate effects under the conditions in which they are strongest - either marginally or within subgroups defined by interactions - as well as covariate-representative tree roots (CRTRs) that provide interpretable visualizations of these conditions. Currently, only classification is supported. This package is a fork of the R package 'ranger' (main author: Marvin N. Wright), which implements random forests using an efficient C++ backend.

Multiple Imputation Using MICE and Random Forest

Functions to impute using random forest under full conditional specifications (multivariate imputation by chained equations). The methods are described in Shah and others (2014) .

Oblique Decision Random Forest for Classification and Regression

The oblique decision tree (ODT) uses linear combinations of predictors as partitioning variables in a decision tree. Oblique Decision Random Forest (ODRF) is an ensemble of multiple ODTs generated by feature bagging. Oblique Decision Boosting Tree (ODBT) applies feature bagging during the training process of ODT-based boosting trees to ensemble multiple boosting trees. All three methods can be used for classification and regression, and ODT and ODRF serve as supplements to the classical CART of Breiman (1984) and Random Forest of Breiman (2001) respectively.

Fast Serializable Random Forests Based on 'ranger'

An updated implementation of R package 'ranger' by Wright et al, (2017) for training and predicting from random forests, particularly suited to high-dimensional data, and for embedding in 'Multiple Imputation by Chained Equations' (MICE) by van Buuren (2007) . Ensembles of classification and regression trees are currently supported. Sparse data of class 'dgCMatrix' (R package 'Matrix') can be directly analyzed. Conventional bagged predictions are available alongside an efficient prediction for MICE via the algorithm proposed by Doove et al (2014) . Trained forests can be written to and read from storage. Survival and probability forests are not supported in the update, nor is data of class 'gwaa.data' (R package 'GenABEL'); use the original 'ranger' package for these analyses.