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: 7.1.0
NeedsCompilation: no
Packaged: 2020-06-29 00:01:36 UTC; priyangatalagala
Author: Priyanga Dilini Talagala [aut, cre]
    (<https://orcid.org/0000-0003-2870-7449>),
  Rob J Hyndman [ths] (<https://orcid.org/0000-0002-2140-5352>),
  Kate Smith-Miles [ths]
Maintainer: Priyanga Dilini Talagala <pritalagala@gmail.com>
Repository: CRAN
Date/Publication: 2020-06-29 04:50:02 UTC
Built: R 4.6.0; ; 2026-04-28 11:54:22 UTC; unix
