outForest: Multivariate Outlier Detection and Replacement

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.

Version: 0.1.3
Depends: R (≥ 3.5.0)
Imports: FNN, ranger, graphics, stats, missRanger (≥ 2.1.0)
Suggests: knitr, rmarkdown, testthat (≥ 3.0.0)
Published: 2023-03-30
Author: Michael Mayer [aut, cre]
Maintainer: Michael Mayer <mayermichael79 at gmail.com>
BugReports: https://github.com/mayer79/outForest/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/mayer79/outForest
NeedsCompilation: no
Materials: README NEWS
CRAN checks: outForest results

Documentation:

Reference manual: outForest.pdf
Vignettes: Using 'outForest'

Downloads:

Package source: outForest_0.1.3.tar.gz
Windows binaries: r-devel: outForest_0.1.2.zip, r-release: outForest_0.1.2.zip, r-oldrel: outForest_0.1.3.zip
macOS binaries: r-release (arm64): outForest_0.1.2.tgz, r-oldrel (arm64): outForest_0.1.2.tgz, r-release (x86_64): outForest_0.1.3.tgz, r-oldrel (x86_64): outForest_0.1.3.tgz
Old sources: outForest archive

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