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outliertree: Explainable Outlier Detection Through Decision Tree Conditioning

Outlier detection method that flags suspicious values within observations, constrasting them against the normal values in a user-readable format, potentially describing conditions within the data that make a given outlier more rare. Full procedure is described in Cortes (2020) <doi:10.48550/arXiv.2001.00636>. Loosely based on the 'GritBot' <https://www.rulequest.com/gritbot-info.html> software.

Version: 1.9.0
Depends: R (≥ 4.3.0)
Imports: Rcpp (≥ 1.0.1), methods
LinkingTo: Rcpp, Rcereal
Suggests: knitr, rmarkdown, kableExtra, data.table
Published: 2024-02-09
Author: David Cortes
Maintainer: David Cortes <david.cortes.rivera at gmail.com>
BugReports: https://github.com/david-cortes/outliertree/issues
License: GPL (≥ 3)
URL: https://github.com/david-cortes/outliertree
NeedsCompilation: yes
CRAN checks: outliertree results

Documentation:

Reference manual: outliertree.pdf
Vignettes: Explainable Outlier Detection in Titanic dataset
Introducing OutlierTree

Downloads:

Package source: outliertree_1.9.0.tar.gz
Windows binaries: r-devel: outliertree_1.9.0.zip, r-release: outliertree_1.9.0.zip, r-oldrel: outliertree_1.9.0.zip
macOS binaries: r-release (arm64): outliertree_1.9.0.tgz, r-oldrel (arm64): outliertree_1.9.0.tgz, r-release (x86_64): outliertree_1.9.0.tgz, r-oldrel (x86_64): outliertree_1.9.0.tgz
Old sources: outliertree archive

Reverse dependencies:

Reverse imports: bagged.outliertrees, itsdm
Reverse suggests: isotree

Linking:

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

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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