ShapleyOutlier: Multivariate Outlier Explanations using Shapley Values and Mahalanobis Distances

Based on Shapley values to explain multivariate outlyingness and to detect and impute cellwise outliers. Includes implementations of methods described in Mayrhofer and Filzmoser (2022) <doi:10.48550/ARXIV.2210.10063>.

Version: 0.1.0
Depends: R (≥ 4.0.0)
Imports: dplyr, Rdpack, stats, tibble, tidyr, robustbase, forcats, egg, ggplot2, gridExtra, RColorBrewer, magrittr
Suggests: grDevices, cellWise, robustHD, tidyverse, knitr, MASS, rmarkdown
Published: 2022-10-21
Author: Marcus Mayrhofer [aut, cre], Peter Filzmoser [aut]
Maintainer: Marcus Mayrhofer <marcus.mayrhofer at>
License: GPL-3
NeedsCompilation: no
Citation: ShapleyOutlier citation info
CRAN checks: ShapleyOutlier results


Reference manual: ShapleyOutlier.pdf
Vignettes: ShapleyOutlier examples


Package source: ShapleyOutlier_0.1.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): ShapleyOutlier_0.1.0.tgz, r-oldrel (arm64): ShapleyOutlier_0.1.0.tgz, r-release (x86_64): ShapleyOutlier_0.1.0.tgz, r-oldrel (x86_64): ShapleyOutlier_0.1.0.tgz


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