Imputation with 'mice' and Distributional Random Forests

Provides a custom imputation method for the 'mice' package based on distributional random forests. The package implements the 'mice.impute.DRF' method, which can be used within the standard 'mice' workflow. Missing values are imputed by estimating conditional distributions with distributional random forests and sampling observed responses using forest weights.

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.

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. (2024) < https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5032102>. 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).

Multivariate Random Forest with Compositional Responses

Multivariate random forests with compositional responses and Euclidean predictors is performed. The compositional data are first transformed using the additive log-ratio transformation, or the alpha-transformation of Tsagris, Preston and Wood (2011), , and then the multivariate random forest of Rahman R., Otridge J. and Pal R. (2017), , is applied.

Adversarial Random Forests for Omics Synthesis

We extend Adversarial Random Forests to a high-dimensional framework. The method partitions the feature space into regions where the assumption of feature independence within tree leaves is more likely to hold. Region-specific adversarial random forest models are trained to capture local dependence structures, while an additional adversarial random forest is fitted to a meta-space representation to model dependencies between regions. New observations are generated by first sampling from the meta-space model and then conditionally sampling from each region-specific model. The proposed methodology is described in Fouodo et al. (2026) .

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.

Intervention in Prediction Measure for Random Forests

Computes intervention in prediction measure for assessing variable importance for random forests. See details at I. Epifanio (2017) .

Random Forest with Canonical Correlation Analysis

Random Forest with Canonical Correlation Analysis (RFCCA) is a random forest method for estimating the canonical correlations between two sets of variables depending on the subject-related covariates. The trees are built with a splitting rule specifically designed to partition the data to maximize the canonical correlation heterogeneity between child nodes. The method is described in Alakus et al. (2021) . 'RFCCA' uses 'randomForestSRC' package (Ishwaran and Kogalur, 2020) by freezing at the version 2.9.3. 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.