Type: Package
Package: outForest
Title: Multivariate Outlier Detection and Replacement
Version: 0.1.2
Date: 2022-01-29
Authors@R: 
    person(given = "Michael",
           family = "Mayer",
           role = c("aut", "cre"),
           email = "mayermichael79@gmail.com")
Maintainer: Michael Mayer <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: 7.1.1
URL: https://github.com/mayer79/outForest
BugReports: https://github.com/mayer79/outForest/issues
Suggests: rmarkdown, knitr, dplyr
VignetteBuilder: knitr
Imports: ranger, FNN, graphics, stats, missRanger (>= 2.1.0)
NeedsCompilation: no
Packaged: 2022-01-29 10:54:26 UTC; Michael
Author: Michael Mayer [aut, cre]
Repository: CRAN
Date/Publication: 2022-01-31 07:40:07 UTC
Built: R 4.0.5; ; 2022-02-01 11:39:33 UTC; unix
