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) <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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.1 |
rolling linux/jammy R-4.5 | stray_0.1.1.tar.gz |
408.1 KiB |
0.1.1 |
rolling linux/noble R-4.5 | stray_0.1.1.tar.gz |
407.8 KiB |
0.1.1 |
rolling source/ R- | stray_0.1.1.tar.gz |
369.2 KiB |
0.1.1 |
latest linux/jammy R-4.5 | stray_0.1.1.tar.gz |
408.1 KiB |
0.1.1 |
latest linux/noble R-4.5 | stray_0.1.1.tar.gz |
407.8 KiB |
0.1.1 |
latest source/ R- | stray_0.1.1.tar.gz |
369.2 KiB |
0.1.1 |
2026-04-26 source/ R- | stray_0.1.1.tar.gz |
369.2 KiB |
0.1.1 |
2026-04-23 source/ R- | stray_0.1.1.tar.gz |
369.2 KiB |
0.1.1 |
2026-04-09 windows/windows R-4.5 | stray_0.1.1.zip |
413.8 KiB |
0.1.1 |
2025-04-20 source/ R- | stray_0.1.1.tar.gz |
369.2 KiB |