{ "_id": "6a4879afe06c18f0a653d421", "Package": "stray", "Type": "Package", "Title": "Anomaly Detection in High Dimensional and Temporal Data", "Version": "0.1.1", "Authors@R": "c(person(\"Priyanga Dilini\", \"Talagala\", email=\"pritalagala@gmail.com\", role= c(\"aut\",\"cre\"), comment = c(ORCID = \"0000-0003-2870-7449\")),\nperson(\"Rob J\", \"Hyndman\", email=\"rob.hyndman@monash.edu\", role=c(\"ths\"), comment = c(ORCID = \"0000-0002-2140-5352\")),\nperson(\"Kate\", \"Smith-Miles\", email=\"smith-miles@unimelb.edu.au\", role=c(\"ths\")))", "Description": "This is a modification of 'HDoutliers' package. The\n'HDoutliers' algorithm is a powerful unsupervised algorithm for\ndetecting anomalies in high-dimensional data, with a strong\ntheoretical foundation. However, it suffers from some\nlimitations that significantly hinder its performance level,\nunder certain circumstances. This package implements the\nalgorithm proposed in Talagala, Hyndman and Smith-Miles (2019)\n for detecting anomalies in high-dimensional\ndata that addresses these limitations of 'HDoutliers'\nalgorithm. We define an anomaly as an observation that deviates\nmarkedly from the majority with a large distance gap. An\napproach based on extreme value theory is used for the\nanomalous 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": { "Date": "2026-07-04 03:07:08 UTC", "User": "root" }, "Author": "Priyanga Dilini Talagala [aut, cre] (ORCID:\n),\nRob J Hyndman [ths] (ORCID: ),\nKate Smith-Miles [ths]", "Maintainer": "Priyanga Dilini Talagala ", "_user": "pridiltal", "_type": "src", "_file": "stray_0.1.1.tar.gz", "_fileid": "https://r2.ropensci.org/0f6abbcd1b29244fd0a9041edf89528b852df730b6cafffd7505b94c6babb1c5", "_filesize": 532802, "_sha256": "0f6abbcd1b29244fd0a9041edf89528b852df730b6cafffd7505b94c6babb1c5", 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