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DESCRIPTION
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DESCRIPTION
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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