sprm: Sparse and Non-Sparse Partial Robust M Regression and Classification

Robust dimension reduction methods for regression and discriminant analysis are implemented that yield estimates with a partial least squares alike interpretability. Partial robust M regression (PRM) is robust to both vertical outliers and leverage points. Sparse partial robust M regression (SPRM) is a related robust method with sparse coefficient estimate, and therefore with intrinsic variable selection. For binary classification related discriminant methods are PRM-DA and SPRM-DA.

Version: 1.2.2
Depends: ggplot2 (≥ 2.0.0)
Imports: cvTools, graphics, grDevices, grid, pcaPP, reshape2, robustbase, stats
Published: 2016-02-22
Author: Sven Serneels (BASF Corp) and Irene Hoffmann
Maintainer: Irene Hoffmann <irene.hoffmann at tuwien.ac.at>
License: GPL (≥ 3)
NeedsCompilation: no
CRAN checks: sprm results

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Reference manual: sprm.pdf
Package source: sprm_1.2.2.tar.gz
Windows binaries: r-devel: sprm_1.2.2.zip, r-devel-gcc8: sprm_1.2.2.zip, r-release: sprm_1.2.2.zip, r-oldrel: sprm_1.2.2.zip
OS X binaries: r-release: sprm_1.2.2.tgz, r-oldrel: sprm_1.2.2.tgz
Old sources: sprm archive

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