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>.

Version:	0.1.0
Depends:	R (≥ 3.6.0)
Imports:	arf, data.table, stats, ClusterR, matrixStats, pracma, pls, fastPLS, RGCCA, ranger, rsvd, foreach
Suggests:	testthat (≥ 3.0.0), knitr, rmarkdown, checkmate, Rtsne, SingleCellExperiment, corrplot, scater, cowplot, ggplot2, doParallel, pROC, caret
Published:	2026-09-28
DOI:	10.32614/CRAN.package.harf
Author:	Cesaire J. K. Fouodo [aut, cre], Jan Kapar [aut], Marvin N. Wright [aut]
Maintainer:	Cesaire J. K. Fouodo <fouodo at leibniz-bips.de>
BugReports:	https://github.com/bips-hb/harf/issues
License:	GPL-3
URL:	https://bips-hb.github.io/harf/
NeedsCompilation:	no
Materials:	README
CRAN checks:	harf results

Documentation:

Reference manual:	harf.html , harf.pdf
Vignettes:	How does harf work? (source, R code)

Downloads:

Package source:	harf_0.1.0.tar.gz
Windows binaries:	r-devel: not available, r-release: harf_0.1.0.zip, r-oldrel: harf_0.1.0.zip
macOS binaries:	r-release (arm64): harf_0.1.0.tgz, r-oldrel (arm64): harf_0.1.0.tgz, r-release (x86_64): harf_0.1.0.tgz, r-oldrel (x86_64): harf_0.1.0.tgz

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