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 (may not be active yet) |
| 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 |