Package: harf 0.1.0

harf: 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) <doi:10.64898/2026.09.09.750490>.

Authors:Cesaire J. K. Fouodo [aut, cre], Jan Kapar [aut], Marvin N. Wright [aut]