stray: Anomaly Detection in High Dimensional and Temporal Data

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) <doi:10.48550/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.

Version: 0.1.1
Depends: R (≥ 3.4.0)
Imports: FNN, ggplot2, colorspace, pcaPP, stats, ks
Published: 2020-06-29
DOI: 10.32614/CRAN.package.stray
Author: Priyanga Dilini Talagala ORCID iD [aut, cre], Rob J Hyndman ORCID iD [ths], Kate Smith-Miles [ths]
Maintainer: Priyanga Dilini Talagala <pritalagala at>
License: GPL-2
NeedsCompilation: no
CRAN checks: stray results


Reference manual: stray.pdf


Package source: stray_0.1.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): stray_0.1.1.tgz, r-oldrel (arm64): stray_0.1.1.tgz, r-release (x86_64): stray_0.1.1.tgz, r-oldrel (x86_64): stray_0.1.1.tgz
Old sources: stray archive

Reverse dependencies:

Reverse imports: weird


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