{ "_id": "6a49b9dbdbd0482285eb797e", "Package": "arf", "Title": "Adversarial Random Forests", "Version": "0.2.5", "Authors@R": "c(\nperson(\"Marvin N.\", \"Wright\", , \"cran@wrig.de\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0002-8542-6291\")),\nperson(\"David S.\", \"Watson\", , \"david.s.watson11@gmail.com\", role = \"aut\",\ncomment = c(ORCID = \"0000-0001-9632-2159\")),\nperson(\"Kristin\", \"Blesch\", role = \"aut\",\ncomment = c(ORCID = \"0000-0001-6241-3079\")),\nperson(\"Jan\", \"Kapar\", role = \"aut\",\ncomment = c(ORCID = \"0009-0000-6408-2840\"))\n)", "Description": "Adversarial random forests (ARFs) recursively partition\ndata into fully factorized leaves, where features are jointly\nindependent. The procedure is iterative, with alternating\nrounds of generation and discrimination. Data becomes\nincreasingly realistic at each round, until original and\nsynthetic samples can no longer be reliably distinguished. This\nis useful for several unsupervised learning tasks, such as\ndensity estimation and data synthesis. Methods for both are\nimplemented in this package. ARFs naturally handle unstructured\ndata with mixed continuous and categorical covariates. They\ninherit many of the benefits of random forests, including\nspeed, flexibility, and solid performance with default\nparameters. For details, see Watson et al. (2023)\n.", "License": "GPL (>= 3)", "URL": "https://github.com/bips-hb/arf, https://bips-hb.github.io/arf/", "BugReports": "https://github.com/bips-hb/arf/issues", "VignetteBuilder": "knitr", "Config/testthat/edition": "3", "Encoding": "UTF-8", "Roxygen": "list(markdown = TRUE)", "RoxygenNote": "7.3.2", "Config/pak/sysreqs": "libicu-dev", "Repository": "https://bips-hb.r-universe.dev", "Date/Publication": "2026-07-05 00:25:06 UTC", "RemoteUrl": "https://github.com/bips-hb/arf", "RemoteRef": "HEAD", "RemoteSha": "f08c562b76144f652db8b3dbcdd156cced0c1291", "NeedsCompilation": "no", "Packaged": { "Date": "2026-07-05 01:52:56 UTC", "User": "root" }, "Author": "Marvin N. Wright [aut, cre] (ORCID:\n),\nDavid S. Watson [aut] (ORCID: ),\nKristin Blesch [aut] (ORCID: ),\nJan Kapar [aut] (ORCID: )", "Maintainer": "Marvin N. 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