doi:10.1109/ICDM.2008.17>), extended isolation forest (Hariri, Kind, Brunner (2018) <doi:10.48550/arXiv.1811.02141>), SCiForest (Liu, Ting, Zhou (2010) <doi:10.1007/978-3-642-15883-4_18>), fair-cut forest (Cortes (2021) <doi:10.48550/arXiv.2110.13402>), robust random-cut forest (Guha, Mishra, Roy, Schrijvers (2016) <http://proceedings.mlr.press/v48/guha16.html>), and customizable variations of them, for isolation-based outlier detection, clustered outlier detection, distance or similarity approximation (Cortes (2019) <doi:10.48550/arXiv.1910.12362>), isolation kernel calculation (Ting, Zhu, Zhou (2018) <doi:10.1145/3219819.3219990>), and imputation of missing values (Cortes (2019) <doi:10.48550/arXiv.1911.06646>), based on random or guided decision tree splitting, and providing different metrics for scoring anomalies based on isolation depth or density (Cortes (2021) <doi:10.48550/arXiv.2111.11639>). Provides simple heuristics for fitting the model to categorical columns and handling missing data, and offers options for varying between random and guided splits, and for using different splitting criteria.">

isotree: Isolation-Based Outlier Detection (original) (raw)

Fast and multi-threaded implementation of isolation forest (Liu, Ting, Zhou (2008) <doi:10.1109/ICDM.2008.17>), extended isolation forest (Hariri, Kind, Brunner (2018) <doi:10.48550/arXiv.1811.02141>), SCiForest (Liu, Ting, Zhou (2010) <doi:10.1007/978-3-642-15883-4_18>), fair-cut forest (Cortes (2021) <doi:10.48550/arXiv.2110.13402>), robust random-cut forest (Guha, Mishra, Roy, Schrijvers (2016) <http://proceedings.mlr.press/v48/guha16.html>), and customizable variations of them, for isolation-based outlier detection, clustered outlier detection, distance or similarity approximation (Cortes (2019) <doi:10.48550/arXiv.1910.12362>), isolation kernel calculation (Ting, Zhu, Zhou (2018) <doi:10.1145/3219819.3219990>), and imputation of missing values (Cortes (2019) <doi:10.48550/arXiv.1911.06646>), based on random or guided decision tree splitting, and providing different metrics for scoring anomalies based on isolation depth or density (Cortes (2021) <doi:10.48550/arXiv.2111.11639>). Provides simple heuristics for fitting the model to categorical columns and handling missing data, and offers options for varying between random and guided splits, and for using different splitting criteria.

Version: 0.6.1-4
Depends: R (≥ 4.3.0)
Imports: Rcpp (≥ 1.0.1), jsonlite (≥ 1.7.3), RhpcBLASctl, methods
LinkingTo: Rcpp
Suggests: MASS, outliertree, DiagrammeR, mlbench, MLmetrics, kernlab, knitr, rmarkdown, kableExtra
Enhances: Matrix, SparseM
Published: 2025-01-08
DOI: 10.32614/CRAN.package.isotree
Author: David Cortes [aut, cre, cph], Thibaut Goetghebuer-Planchon [cph] (Copyright holder of included robinmap library), David Blackman [cph] (Copyright holder of original xoshiro code), Sebastiano Vigna [cph] (Copyright holder of original xoshiro code), NumPy Developers [cph] (Copyright holder of formatted ziggurat tables), SciPy Developers [cph] (Copyright holder of parts of digamma implementation), Enthought Inc [cph] (Copyright holder of parts of digamma implementation), Stephen Moshier [cph] (Copyright holder of parts of digamma implementation)
Maintainer: David Cortes <david.cortes.rivera at gmail.com>
BugReports: https://github.com/david-cortes/isotree/issues
License: BSD_2_clause + file
Copyright: see file
URL: https://github.com/david-cortes/isotree
NeedsCompilation: yes
In views: AnomalyDetection, MissingData
CRAN checks: isotree results

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