[R-pkgs] RobASt-Packages

From: Matthias Kohl <Matthias.Kohl_at_stamats.de>
Date: Mon, 15 Sep 2008 12:14:50 +0200

Packages for the computation of optimally robust estimators

We would like to announce the availability on CRAN (with possibly a minor delay until on every mirror) of new versions of our packages for the computation of optimally robust estimators; i.e., "RandVar", "ROptEst", "RobLox" as well as a new package "RobAStBase" (not yet: ROptRegTS and RobRex).

Devel versions on R-forge

The development of these packages is under r-forge project RobASt (Robust Asymptotic Statistics):

http://r-forge.r-project.org/projects/robast/ http://robast.r-forge.r-project.org/

If you find this project interesting and would like to collaborate, you are warmly welcome.

We look forward to receiving questions, comments and suggestions.

Matthias Kohl
Peter Ruckdeschel

RandVar - Implementation of random variables (version 0.6.3)

The package RandVar which includes an S4 implementation of random variables together with the packages distr, distrEx and distrMod form the basis of our packages on robust statistics.

RobAStBase - Robust Asymptotic Statistics (version 0.1.0)

This is a new package including some necessary S4 class infrastructure like neighborhoods, influence curves and robust models.

ROptEst - Optimally robust estimation (version 0.6.0)

This is the main package for the optimally robust estimation in smoothly (L2-differentiable) parametric models [optimal in the sense of the shrinking neighborhood setup]. By using S4 classes and methods the implementation so far covers the optimally robust estimation for all(!) smoothly (L2-differentiable/differentiable in quadratic mean) parametric models which are based on a univariate distribution. Many well-known parametric (in particular, exponential) families (Binomial, Poission, Normal, Gamma, Gumbel, ...) are L2-differentiable. We include several
   +neighborhood types (convex contamination, total variation)
   +risks (MSE, Hampel, overshoot/undershoot),
   +bias-types (symmetric, one-sided, asymmetric)
   +norms (unstandardized, self-standardized, information-standardized)
for all these models.
After installation you find a folder "scripts" in the package directory which includes many example scripts.
As the computation of optimally robust estimators involves several steps, we -- in this new version -- added an interface function "roptest" which can be used to perform all steps via one function.

RobLox - Optimally robust influence curves for location and scale (version 0.6.0)

This package includes functions for the computation of many well known influence curves (e.g., Huber-, Hampel-, Tukey-, Andrews-type) for normal location and scale in the framework of our asymptotic setup. Moreover, (and for us, more importantly) it includes the functions "roblox", "rowRoblox" and "colRoblox" which can be used to compute optimally robust estimators in case of normal location and scale. These functions are optimized for speed and can be applied to large scale problems like for instance gene expression data. Using rowRobLox the computation for a 50000 x 20 matrix takes about 2 sec. on a Centrino Duo with 1.66 GHz. As a comparison (all on the same system): using apply and huberM (robustbase), resp. huber (MASS) takes about 168 sec. resp 197 sec., using apply and roblox takes about 16 minutes and using apply and roptest (ROptEst) takes about 1 month.

ROptRegTS - Optimally robust estimation for regression-type models RobRex - Optimally robust influence curves for regression and scale

These two packages which provide S4 classes and methods for the computation of optimally robust estimators in regression-type models are not yet adapted to the new implementation. If you are interested in working with these packages you have to use the old versions of the above packages which we are pleased to provide on request (the sources can also be found in the CRAN archives). But, of course, we will try to update these packages as soon as possible.
Dr. Matthias Kohl

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Received on Tue 16 Sep 2008 - 04:40:50 EST

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