Analyzing Data with Cellwise Outliers

Tools for detecting cellwise outliers and robust methods to analyze data which may contain them. Contains the implementation of the algorithms described in Rousseeuw and Van den Bossche (2018) (open access) Hubert et al. (2019) (open access), Raymaekers and Rousseeuw (2019) (open access), Raymaekers and Rousseeuw (2020) (open access), Raymaekers and Rousseeuw (2020) (open access). Examples can be found in the vignettes: "DDC_examples", "MacroPCA_examples", "wrap_examples", "transfo_examples" and "DI_examples".


Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.


2.2.5 by Jakob Raymaekers, a year ago

Browse source code at

Authors: Jakob Raymaekers [aut, cre] , Peter Rousseeuw [aut] , Wannes Van den Bossche [ctb] , Mia Hubert [ctb]

Documentation:   PDF Manual  

GPL (>= 2) license

Imports reshape2, scales, ggplot2, matrixStats, gridExtra, robustbase, rrcov, svd, stats, Rcpp

Suggests knitr, robustHD, MASS, ellipse, markdown, rospca, GSE

Linking to Rcpp, RcppArmadillo

Imported by GSE, OutliersO3, RDnp, classmap.

Suggested by wbacon.

See at CRAN