Robust statistical methods are powerful tools to increase the reliability and accuracy of statistical modeling and data analysis because these methods still work well when some observations deviate from (or violate) the assumed model. For many statistical models (e.g. location, scale and regression), several robust methods are available as alternative approaches to classical statistical methods (Maronna et al., 2006). However, there has been a lack of availability of robust methods in statistical software (e.g. Stromberg, 2004). The R package 'robustbase' (as well as other related packages) helps to fill the gap between theoretically developed robust methods and their availability in standard statistical software and makes both basic and advanced methods available to a broad range of researchers.
'robustbase' is an add-on package for the open-source statistical software R, a language and environment for statistical computing. Brief introductions to R are given, for instance, by Cribari-Neto and Zarkos (1999) and Racine and Hyndman (2002). The 'robustbase' package was written by Valentin Todorov, Andreas Ruckstuhl, Matias Salibian-Barrera, Tobias Verbeke and Martin Maechler. 1 It was developed to make 'basic robust statistics' within R available in a single package and to provide tools that allow analyzing data with robust methods. Furthermore, 'robustbase' implements new robust methods, which have not been available so far in R (Maechler and Ruckstuhl, 2006).
A further comprehensive add-on package on robust statistics is the 'robust' package, which is a version of the robust library of S-PLUS made available in R 2 . This package covers similar topics and partially overlaps with 'robustbase'. Coordination effort is made to develop the 'robust' package as a supplement that provides convenient routines and comparisons with classical estimators. In contrast, 'robustbase' should provide the more advanced statistician with a considerable choice of methodology (Maechler, 2008). A complement to 'robustbase' is the R package 'RobustX' (written by Werner Stahel and Martin Maechler), which is especially designed