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:	1.0.1
Depends:	R (≥ 3.5.0)
Imports:	FNN, ranger, graphics, stats, missRanger (≥ 2.1.0)
Suggests:	knitr, rmarkdown, testthat (≥ 3.0.0)
Published:	2023-05-21
DOI:	10.32614/CRAN.package.outForest
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_1.0.1.tar.gz
Windows binaries:	r-devel: outForest_1.0.1.zip, r-release: outForest_1.0.1.zip, r-oldrel: outForest_1.0.1.zip
macOS binaries:	r-release (arm64): outForest_1.0.1.tgz, r-oldrel (arm64): outForest_1.0.1.tgz, r-release (x86_64): outForest_1.0.1.tgz, r-oldrel (x86_64): outForest_1.0.1.tgz
Old sources:	outForest archive

Linking:

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