DESCRIPTION

Package: isotree
Type: Package
Title: Isolation-Based Outlier Detection
Version: 0.6.1-2
Authors@R: c(
  person(given="David", family="Cortes", role=c("aut", "cre", "cph"),
         email="david.cortes.rivera@gmail.com"),
  person(given="Thibaut", family="Goetghebuer-Planchon", role="cph",
         comment="Copyright holder of included robinmap library"),
  person(given="David", family="Blackman", role="cph",
         comment="Copyright holder of original xoshiro code"),
  person(given="Sebastiano", family="Vigna", role="cph",
         comment="Copyright holder of original xoshiro code"),
  person(given="NumPy", family="Developers", role="cph",
         comment="Copyright holder of formatted ziggurat tables"),
  person(given="SciPy", family="Developers", role="cph",
         comment="Copyright holder of parts of digamma implementation"),
  person(given="Enthought", family="Inc", role="cph",
         comment="Copyright holder of parts of digamma implementation"),
  person(given="Stephen", family="Moshier", role="cph",
         comment="Copyright holder of parts of digamma implementation")
  )
Maintainer: David Cortes <david.cortes.rivera@gmail.com>
URL: https://github.com/david-cortes/isotree
BugReports: https://github.com/david-cortes/isotree/issues
Description: 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) <arXiv:1811.02141>),
	SCiForest (Liu, Ting, Zhou (2010) <doi:10.1007/978-3-642-15883-4_18>),
	fair-cut forest (Cortes (2021) <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) <arXiv:1910.12362>),
	isolation kernel calculation (Ting, Zhu, Zhou (2018) <doi:10.1145/3219819.3219990>),
	and imputation of missing values (Cortes (2019) <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) <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.
License: BSD_2_clause + file LICENSE
Depends: R (>= 4.3.0)
Imports: Rcpp (>= 1.0.1), jsonlite (>= 1.7.3), RhpcBLASctl, methods
Suggests: MASS, outliertree, DiagrammeR,
    mlbench, MLmetrics, kernlab, knitr, rmarkdown, kableExtra
Enhances: Matrix, SparseM
LinkingTo: Rcpp
VignetteBuilder: knitr
RoxygenNote: 7.3.2