arf: Adversarial Random Forests
Adversarial random forests (ARFs) recursively partition data
into fully factorized leaves, where features are jointly independent.
The procedure is iterative, with alternating rounds of generation and
discrimination. Data becomes increasingly realistic at each round,
until original and synthetic samples can no longer be reliably
distinguished. This is useful for several unsupervised learning
tasks, such as density estimation and data synthesis. Methods for both
are implemented in this package. ARFs naturally handle unstructured
data with mixed continuous and categorical covariates. They inherit
many of the benefits of random forests, including speed, flexibility,
and solid performance with default parameters. For details, see
Watson et al. (2023)
<https://proceedings.mlr.press/v206/watson23a.html>.
| Version: |
0.2.5 |
| Imports: |
data.table, foreach, ranger, stringr, truncnorm |
| Suggests: |
doFuture, doParallel, ggplot2, knitr, mlbench, palmerpenguins, rmarkdown, testthat (≥ 3.0.0), tibble |
| Published: |
2026-09-21 |
| DOI: |
10.32614/CRAN.package.arf |
| Author: |
Marvin N. Wright 
[aut, cre],
David S. Watson
[aut],
Kristin Blesch
[aut],
Jan Kapar [aut],
Lukas Burk [ctb],
Martin Jullum
[ctb] |
| Maintainer: |
Marvin N. Wright <cran at wrig.de> |
| BugReports: |
https://github.com/bips-hb/arf/issues |
| License: |
GPL (≥ 3) |
| URL: |
https://github.com/bips-hb/arf, https://bips-hb.github.io/arf/ |
| NeedsCompilation: |
no |
| Citation: |
arf citation info |
| Materials: |
README, NEWS |
| CRAN checks: |
arf results |
Documentation:
Downloads:
Reverse dependencies:
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