Outlier Detection Using the Iterated RMCD Method of Cerioli (2010)
Overview
Implements the iterated RMCD method of Cerioli (2010) for multivariate outlier detection via robust Mahalanobis distances. Also provides the finite-sample RMCD method discussed in the paper, as well as the methods provided in Hardin and Rocke (2005) doi:10.1198/106186005X77685 and Green and Martin (2017) https://christopherggreen.github.io/papers/hr05_extension.pdf. See also Chapter 2 of Green (2017) https://digital.lib.washington.edu/researchworks/handle/1773/40304.
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK14 OK2026-03-10
Documentation
- Help pages
- 7includes a package overview page
- Examples that run
- 100%6 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 93%
- Return-value docs
- 100%
- Help pages citing references
- 100%
Downloads
Repository
1 development-tooling and community-health practice detected across 1 family in the upstream repository
Checks run against github.com/christopherggreen/ceriolioutlierdetection on 2026-10-22.
GitHub Pages site last deployed 2017-07-24
Dependencies
Installing CerioliOutlierDetection adds 2 packages (1 with compiled code, 2.2 MB of source tarballs). Heaviest: robustbase 2.2 MB, DEoptimR 24 KB.
Code & Tests
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- RR 4.6.0 released · 2026-04-24
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- RR 3.6.0 released · 2019-04-26
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- RR 3.4.0 released · 2017-04-21
- 1.1.52016-07-08 · diff ↗
- RR 3.3.0 released · 2016-05-03
- RR 3.2.0 released · 2015-04-16
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- 1.0.82014-08-02
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Package metadata
- First published
- 2014-08-02
- Total releases
- 5 / 12 yrs
- License
- GPL (>= 2) OSI
- Minimum R
- ≥ 4.0.0
- Download size
- 14 KB
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