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Welcome |
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LIBRA: a MATLAB Library for Robust Analysis is developed at the
research groups on robust statistics at the
Katholieke
Universiteit Leuven and the
University of Antwerp.
It contains user-friendly implementations of several robust procedures
which are developed at both research groups. These methods are resistant
to outliers in the data. Currently, the library contains functions for univariate
location, scale and skewness, multivariate location and covariance estimation (MCD), regression
(LTS, MCD-regression), Principal Component Analysis (RAPCA, ROBPCA),
Principal Component Regression (RPCR), Partial Least Squares Regression (RSIMPLS), classification (RDA, RSIMCA), clustering,
outlier detection for skewed data (including the bagplot based on halfspace depth), and censored depth quantiles.
For comparison also several non-robust functions are
included. Many graphical tools are provided for model checking and outlier
detection.
 List of functions (PDF file)
This library can be used with MATLAB 5.2, 6.1, 6.5, and 7.0. Most of the functions require the MATLAB Statistics Toolbox.
Contributions to this library have been made by (in alphabetical
order):
Guy Brys, Michiel Debruyne, Sanne
Engelen, Mia Hubert, Wai Yan Kong, Nele Smets, Karlien Vanden Branden, Stephan Van der Veeken, Ellen Vandervieren,
Katrien Van Driessen, Sabine Verboven, Tim Verdonck en Fabienne Verwerft.
The library can be freely used for non-commercial use only.
Please make appropriate references to the corresponding paper(s)
if you use any of our programs. The correct references can be
found in the help-files, or at the webpages:
http://wis.kuleuven.be/stat/robust
http://www.agoras.ua.ac.be/
More details on the use of the library are described in:
Verboven, S., Hubert, M. (2005), LIBRA: a MATLAB Library for Robust Analysis,
Chemometrics and Intelligent Laboratory Systems, 75, 127-136.
[paper (pdf)]
Bugs or comments on the programs can be reported to
Sabine Verboven.
Several functions of the LIBRA Toolbox are also available in the PLS Toolbox at Eigenvector Research.
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Download LIBRA for
Academic users
Non-academic users
Last update of LIBRA: October 20, 2009
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