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Skewness plays a relevant role in several multivariate statisticaltechniques. Sometimes it is used to recover data features, as in clusteranalysis. In other circumstances, skewness impairs the performances ofstatistical methods, as in the Hotelling's one-sample test. In both cases,there is the need to check the symmetry of the underlying distribution, eitherby visual inspection or by formal testing. The R packages MaxSkew and MultiSkewaddress these issues by measuring, testing and removing skewness frommultivariate data. Skewness is assessed by the third multivariate cumulant andits functions. The hypothesis of symmetry is tested either nonparametrically,with the bootstrap, or parametrically, under the normality assumption. Skewnessis removed or at least alleviated by projecting the data onto appropriatelinear subspaces. Usages of MaxSkew and MultiSkew are illustrated with the Irisdataset.

MaxSkew and MultiSkew: Two R Packages for Detecting, Measuring and Removing Multivariate Skewness