Package: outForest
Title: Multivariate Outlier Detection and Replacement
Version: 1.0.1
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
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
NeedsCompilation: no
Packaged: 2023-05-21 18:33:39 UTC; Michael
Author: Michael Mayer [aut, cre]
Maintainer: Michael Mayer <mayermichael79@gmail.com>
Repository: CRAN
Date/Publication: 2023-05-21 18:50:02 UTC
Built: R 4.6.0; ; 2026-04-27 19:05:43 UTC; unix
