CRAN Task View: Robust Statistical Methods

Maintainer:Martin Maechler
Contact:Martin.Maechler at

Robust (or "resistant") methods for statistics modelling have been available in S from the start, in R in package stats (e.g., median(), mean(*, trim = . ), mad(), IQR(), or also fivenum() (the statistic behind boxplot() in package graphics) or lowess(), which had been complemented by runmed() in 2003. Much further important functionality has been made available in package MASS (by Bill Venables and Brian Ripley, see the book Modern Applied Statistics with S ), notably rlm() for robust regression and cov.rob() for robust multivariate scatter and covariance.

This task view is about R add-on packages providing newer or faster, more efficient algorithms and notably for (robustification of) new models.

Please send suggestions for additions and extensions to the task view maintainer .

An international group of scientists working in the field of robust statistics has made efforts (since October 2005) to coordinate several of the scattered developments and make the important ones available through a set of R packages complementing each other. These should build on a basic package with "Essentials", coined robustbase with (potentially many) other packages building on top and extending the essential functionality to particular models and or applications. Further, there is the quite comprehensive package robust, a version of the robust library of S-PLUS, as an R package now GPLicensed thanks to Insightful and Kjell Konis. Whereas there is currently quite a bit of overlap (between 'robustbase' and 'robust'), the intent (as of Nov.2007) is that robust will eventually depend on robustbase, the former providing convenient routines for the casual user where the latter will contain the underlying functionality, and provide the more advanced statistician with a considerable choice of methodology.

We structure the packages roughly into the following topics, and typically will first mention functionality in packages robustbase and robust.

CRAN packages:

Related links: