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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: | 0.1.1 |
Depends: | R (≥ 4.0.0) |
Imports: | dplyr, Rdpack, stats, tibble, tidyr, robustbase, forcats, egg, ggplot2, gridExtra, RColorBrewer, magrittr |
Suggests: | grDevices, cellWise, robustHD, knitr, MASS, rmarkdown |
Published: | 2023-02-20 |
DOI: | 10.32614/CRAN.package.ShapleyOutlier |
Author: | Marcus Mayrhofer [aut, cre], Peter Filzmoser [aut] |
Maintainer: | Marcus Mayrhofer <marcus.mayrhofer at tuwien.ac.at> |
License: | GPL-3 |
NeedsCompilation: | no |
Citation: | ShapleyOutlier citation info |
CRAN checks: | ShapleyOutlier results |
Reference manual: | ShapleyOutlier.pdf |
Vignettes: |
ShapleyOutlier examples |
Package source: | ShapleyOutlier_0.1.1.tar.gz |
Windows binaries: | r-devel: ShapleyOutlier_0.1.1.zip, r-release: ShapleyOutlier_0.1.1.zip, r-oldrel: ShapleyOutlier_0.1.1.zip |
macOS binaries: | r-release (arm64): ShapleyOutlier_0.1.1.tgz, r-oldrel (arm64): ShapleyOutlier_0.1.1.tgz, r-release (x86_64): ShapleyOutlier_0.1.1.tgz, r-oldrel (x86_64): ShapleyOutlier_0.1.1.tgz |
Old sources: | ShapleyOutlier archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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