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.


Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.


1.2.2 by Irene Hoffmann, 4 years ago

Browse source code at

Authors: Sven Serneels (BASF Corp) and Irene Hoffmann

Documentation:   PDF Manual  

GPL (>= 3) license

Imports cvTools, graphics, grDevices, grid, pcaPP, reshape2, robustbase, stats

Depends on ggplot2

See at CRAN