Detection of Outliers in High Dimensional Data
Overview
Algorithms to detect high-dimensional outliers. The minimum diagonal product of Ro, Zou, Wang and Yin (2015) doi:10.1093/biomet/asv021, the algorithm of Wilkinson (2018) doi:10.1109/TVCG.2017.2744685, and the distances of distances of Lee and Jeon (2025) doi:10.48550/arXiv.2511.02199.
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK6 OK2026-07-05
Documentation
- Help pages
- 4includes a package overview page
- Examples that run
- 100%3 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- not tracked
- Return-value docs
- not tracked
- Help pages citing references
- 100%
Downloads
Dependencies
Installing outliersHD adds 8 packages (7 with compiled code, 18 MB of source tarballs). Heaviest: BH 14 MB, Rcpp 1.8 MB, RcppArmadillo 1.0 MB.
Code & Tests
People & History
- 1.0Latest2026-07-04 · current release
- RR 4.6.0 released · 2026-04-24
Package metadata
- First published
- 2026-07-04
- Total releases
- 1 / 1 yrs
- License
- GPL (>= 2) OSI
- Minimum R
- ≥ 4.0
- Download size
- 3.6 KB
Cite
Cite this package
Run in R for the authors' preferred citation:
citation("outliersHD")This package has no citation file, so citation() returns this entry built from DESCRIPTION.
Cite the R Observatory
For a number measured here: a download total, a coverage figure, an archival date.
From data release v2026-10-24, which the citation names so these numbers can be found later. More on citing and the projects behind them.