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.acm.org/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: 0.1.0
Depends: R (≥ 3.5.0)
Imports: stats, graphics, FNN, ranger, missRanger (≥ 2.1.0)
Suggests: dplyr, knitr
Published: 2020-01-13
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


Reference manual: outForest.pdf
Vignettes: outForest
Package source: outForest_0.1.0.tar.gz
Windows binaries: r-prerelease: outForest_0.1.0.zip, r-release: outForest_0.1.0.zip, r-oldrel: outForest_0.1.0.zip
macOS binaries: r-prerelease: outForest_0.1.0.tgz, r-release: outForest_0.1.0.tgz, r-oldrel: outForest_0.1.0.tgz


Please use the canonical form https://CRAN.R-project.org/package=outForest to link to this page.