# -------------------------------------------- # CITATION file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # -------------------------------------------- cff-version: 1.2.0 message: 'To cite package "stray" in publications use:' type: software license: GPL-2.0-only title: 'stray: Anomaly Detection in High Dimensional and Temporal Data' version: 0.1.1 doi: 10.32614/CRAN.package.stray abstract: 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) 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. authors: - family-names: Talagala given-names: Priyanga Dilini email: pritalagala@gmail.com orcid: https://orcid.org/0000-0003-2870-7449 repository: https://robjhyndman.r-universe.dev repository-code: https://github.com/pridiltal/stray commit: 519b2e05b76ef9644131386f039767acf52b8124 url: https://github.com/pridiltal/stray contact: - family-names: Talagala given-names: Priyanga Dilini email: pritalagala@gmail.com orcid: https://orcid.org/0000-0003-2870-7449