The present work discusses robust multivariate methods specifically designed for high
dimensions. Their implementation in R is presented and their application is illustrated
on examples. The first group are algorithms for outlier detection, already introduced
elsewhere and implemented in other packages. The value added of the new package is
that all methods follow the same design pattern and thus can use the same graphical
and diagnostic tools. The next topic covered is sparse principal components including an
object oriented interface to the standard method proposed by Zou, Hastie, and Tibshirani
(2006) and the robust one proposed by Croux, Filzmoser, and Fritz (2013). Robust partial
least squares (see Hubert and Vanden Branden 2003) as well as partial least squares for
discriminant analysis conclude the scope of the new package.