Type: Package
Package: outForest
Title: Multivariate Outlier Detection and Replacement
Version: 0.1.1
Date: 2021-01-06
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)
URL: https://github.com/mayer79/outForest
BugReports: https://github.com/mayer79/outForest/issues
Depends: R (>= 3.5.0)
VignetteBuilder: knitr
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
Imports: stats, graphics, FNN, ranger, missRanger (>= 2.1.0)
Suggests: dplyr, knitr, rmarkdown
NeedsCompilation: no
Packaged: 2021-01-06 07:24:15 UTC; Michael
Author: Michael Mayer [aut, cre]
Repository: CRAN
Date/Publication: 2021-01-07 02:50:02 UTC
Built: R 3.6.3; ; 2021-05-12 19:40:22 UTC; windows
