Package: CerioliOutlierDetection 1.1.13

CerioliOutlierDetection: Outlier Detection Using the Iterated RMCD Method of Cerioli (2010)

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).

Authors:Christopher G. Green [aut, cre], R. Doug Martin [ths]

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CerioliOutlierDetection.pdf |CerioliOutlierDetection.html✨
CerioliOutlierDetection/json (API)

# Install 'CerioliOutlierDetection' in R:
install.packages('CerioliOutlierDetection', repos = c('https://christopherggreen.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/christopherggreen/ceriolioutlierdetection/issues

On CRAN:

Conda:

3.11 score 10 stars 13 scripts 329 downloads 6 exports 2 dependencies

Last updated 1 years agofrom:cb1764f61f. Checks:6 OK, 3 NOTE. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 22 2025
R-4.5-winNOTEMar 22 2025
R-4.5-macNOTEMar 22 2025
R-4.5-linuxNOTEMar 22 2025
R-4.4-winOKMar 22 2025
R-4.4-macOKMar 22 2025
R-4.4-linuxOKMar 22 2025
R-4.3-winOKMar 22 2025
R-4.3-macOKMar 22 2025

Exports:cerioli2010.fsrmcd.testcerioli2010.irmcd.testch99AsymptoticDFhr05AdjustedDFhr05CriticalValuehr05CutoffMvnormal

Dependencies:DEoptimRrobustbase