Package: stray
Type: Package
Title: Anomaly Detection in High Dimensional and Temporal Data
Version: 0.1.1
Depends: R (>= 3.4.0)
Imports: FNN, ggplot2, colorspace, pcaPP, stats, ks
Authors@R: 
    c(person("Priyanga Dilini", "Talagala", email="pritalagala@gmail.com", role= c("aut","cre"), comment = c(ORCID = "0000-0003-2870-7449")),
    person("Rob J", "Hyndman", email="rob.hyndman@monash.edu", role=c("ths"), comment = c(ORCID = "0000-0002-2140-5352")),
    person("Kate", "Smith-Miles", email="smith-miles@unimelb.edu.au", role=c("ths")))
Description: This is a modification of 'HDoutliers' package. The
        'HDoutliers' algorithm is a powerful unsupervised algorithm for
        detecting anomalies in high-dimensional data, with a strong
        theoretical foundation. However, it suffers from some
        limitations that significantly hinder its performance level,
        under certain circumstances. This package implements the
        algorithm proposed in Talagala, Hyndman and Smith-Miles (2019)
        <arXiv:1908.04000> for detecting anomalies in high-dimensional
        data that addresses these limitations of 'HDoutliers'
        algorithm. We define an anomaly as an observation that deviates
        markedly from the majority with a large distance gap. An
        approach based on extreme value theory is used for the
        anomalous threshold calculation.
BugReports: https://github.com/pridiltal/stray/issues
License: GPL-2
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.2.3
Repository: https://pridiltal.r-universe.dev
Date/Publication: 2023-11-19 23:14:31 UTC
RemoteUrl: https://github.com/pridiltal/stray
RemoteRef: HEAD
RemoteSha: 519b2e05b76ef9644131386f039767acf52b8124
NeedsCompilation: no
Packaged: 2026-07-04 03:07:08 UTC; root
Author: Priyanga Dilini Talagala [aut, cre] (ORCID:
    <https://orcid.org/0000-0003-2870-7449>),
  Rob J Hyndman [ths] (ORCID: <https://orcid.org/0000-0002-2140-5352>),
  Kate Smith-Miles [ths]
Maintainer: Priyanga Dilini Talagala <pritalagala@gmail.com>