Skewness plays a relevant role in several multivariate statistical
techniques. Sometimes it is used to recover data features, as in cluster
analysis. In other circumstances, skewness impairs the performances of
statistical methods, as in the Hotelling's one-sample test. In both cases,
there is the need to check the symmetry of the underlying distribution, either
by visual inspection or by formal testing. The R packages MaxSkew and MultiSkew
address these issues by measuring, testing and removing skewness from
multivariate data. Skewness is assessed by the third multivariate cumulant and
its functions. The hypothesis of symmetry is tested either nonparametrically,
with the bootstrap, or parametrically, under the normality assumption. Skewness
is removed or at least alleviated by projecting the data onto appropriate
linear subspaces. Usages of MaxSkew and MultiSkew are illustrated with the Iris
dataset
