Package: outForest
Title: Multivariate Outlier Detection and Replacement
Version: 1.0.2
Authors@R: 
    person(given = "Michael",
           family = "Mayer",
           role = c("aut", "cre"),
           email = "mayermichael79@gmail.com")
Description: Provides a random forest based implementation of the
        method described in Chapter 7.1.2 (Regression model based
        anomaly detection) of Chandola et al. (2009)
        <doi:10.1145/1541880.1541882>. It works as follows: Each
        numeric variable is regressed onto all other variables by a
        random forest. If the scaled absolute difference between
        observed value and out-of-bag prediction of the corresponding
        random forest is suspiciously large, then a value is considered
        an outlier. The package offers different options to replace
        such outliers, e.g. by realistic values found via predictive
        mean matching. Once the method is trained on a reference data,
        it can be applied to new data.
License: GPL (>= 2)
Depends: R (>= 3.5.0)
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.2.3
Imports: FNN, ranger, graphics, stats, missRanger (>= 2.1.0)
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
URL: https://github.com/mayer79/outForest
BugReports: https://github.com/mayer79/outForest/issues
VignetteBuilder: knitr
Config/testthat/edition: 3
Repository: https://mayer79.r-universe.dev
Date/Publication: 2025-04-06 09:47:38 UTC
RemoteUrl: https://github.com/mayer79/outforest
RemoteRef: HEAD
RemoteSha: 33820d491475455c04679c424c4d01dd42755898
NeedsCompilation: no
Packaged: 2026-06-14 10:22:22 UTC; root
Author: Michael Mayer [aut, cre]
Maintainer: Michael Mayer <mayermichael79@gmail.com>