R tools for robust statistical analysis of high–dimensional data

Abstract

The present work discusses robust multivariate methods specifically designed for high dimensions. Their implementation in R is presented and their appli- cation is illustrated on examples. The first group of classes are algorithms for outlier detection, already introduced elsewhere and implemented in other pack- ages. The value added of the new package is that all methods follow the same 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 et al [14] and the robust one proposed by Croux et al [2]. Robust partial least squares (Hubert and Vanden Branden [6]) as well as partial least squares for discriminant analysis conclude the scope of the new package.