robustDA: Robust Mixture Discriminant Analysis

Robust mixture discriminant analysis (RMDA, Bouveyron & Girard, 2009) allows to build a robust supervised classifier from learning data with label noise. The idea of the proposed method is to confront an unsupervised modeling of the data with the supervised information carried by the labels of the learning data in order to detect inconsistencies. The method is able afterward to build a robust classifier taking into account the detected inconsistencies into the labels.

Version: 1.1
Depends: MASS, mclust, Rsolnp
Published: 2015-01-14
Author: Charles Bouveyron & Stephane Girard
Maintainer: Charles Bouveyron <charles.bouveyron at>
License: GPL-2
NeedsCompilation: no
In views: Robust
CRAN checks: robustDA results


Reference manual: robustDA.pdf
Package source: robustDA_1.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X Mavericks binaries: r-release: robustDA_1.1.tgz, r-oldrel: robustDA_1.1.tgz
Old sources: robustDA archive


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