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isotree  

Isolation-Based Outlier Detection
View on CRAN: Click here


Download and install isotree package within the R console
Install from CRAN:
install.packages("isotree")

Install from Github:
library("remotes")
install_github("cran/isotree")

Install by package version:
library("remotes")
install_version("isotree", "0.6.1-1")



Attach the package and use:
library("isotree")
Maintained by
David Cortes
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-12-18
Latest Update: 2023-08-07
Description:
Fast and multi-threaded implementation of isolation forest (Liu, Ting, Zhou (2008) ), extended isolation forest (Hariri, Kind, Brunner (2018) ), SCiForest (Liu, Ting, Zhou (2010) ), fair-cut forest (Cortes (2021) ), robust random-cut forest (Guha, Mishra, Roy, Schrijvers (2016) ), and customizable variations of them, for isolation-based outlier detection, clustered outlier detection, distance or similarity approximation (Cortes (2019) ), isolation kernel calculation (Ting, Zhu, Zhou (2018) ), and imputation of missing values (Cortes (2019) ), based on random or guided decision tree splitting, and providing different metrics for scoring anomalies based on isolation depth or density (Cortes (2021) ). 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.
How to cite:
David Cortes (2019). isotree: Isolation-Based Outlier Detection. R package version 0.6.1-1, https://cran.r-project.org/web/packages/isotree
Previous versions and publish date:
0.1.5 (2019-12-18 16:20), 0.1.8 (2020-01-08 13:40), 0.1.11 (2020-04-27 18:20), 0.1.17 (2020-07-20 11:20), 0.1.18 (2020-07-29 20:30), 0.1.19 (2020-10-11 14:10), 0.1.20 (2020-10-14 01:50), 0.1.24 (2020-12-10 16:30), 0.1.28 (2021-01-13 21:20), 0.2.7 (2021-03-01 20:20), 0.2.8 (2021-07-11 06:20), 0.2.10 (2021-07-17 08:40), 0.3.0 (2021-08-22 10:30), 0.3.9 (2021-10-23 19:40), 0.5.5 (2022-01-11 10:12), 0.5.14 (2022-02-13 20:00), 0.5.15 (2022-03-31 12:40), 0.5.16 (2022-08-06 22:00), 0.5.17 (2022-09-07 19:40), 0.5.19-1 (2023-03-16 20:40), 0.5.20 (2023-05-23 09:10), 0.5.22 (2023-08-07 18:00), 0.5.24-1 (2023-11-28 08:20), 0.5.24-2 (2023-12-01 11:20), 0.5.24-3 (2023-12-02 04:20), 0.5.24 (2023-11-19 23:40)
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