Routliers: Robust Outliers Detection

Detecting outliers using robust methods, i.e. the Median Absolute Deviation (MAD) for univariate outliers; Leys, Ley, Klein, Bernard, & Licata (2013) <doi:10.1016/j.jesp.2013.03.013> and the Mahalanobis-Minimum Covariance Determinant (MMCD) for multivariate outliers; Leys, C., Klein, O., Dominicy, Y. & Ley, C. (2018) <doi:10.1016/j.jesp.2017.09.011>. There is also the more known but less robust Mahalanobis distance method, only for comparison purposes.

Depends: R (≥ 2.10)
Imports: MASS, stats, graphics, ggplot2
Suggests: knitr, rmarkdown, testthat
Published: 2019-05-23
Author: Marie Delacre [aut, cre], Olivier Klein [aut]
Maintainer: Marie Delacre <marie.delacre at>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README NEWS
CRAN checks: Routliers results


Reference manual: Routliers.pdf
Package source: Routliers_0.0.0.3.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X binaries: r-release: Routliers_0.0.0.3.tgz, r-oldrel: Routliers_0.0.0.3.tgz
Old sources: Routliers archive


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