Shrinkage Discriminant Analysis and Feature Selection

This package provides an efficient framework for high-dimensional linear and diagonal discriminant analysis with feature selection. The classifier is trained using James-Stein-type shrinkage estimators and features are ranked using correlation-adjusted t-scores. Feature selection is controlled using false non-discovery rates or higher criticism scores.

Version: 1.1.0

Authors: Miika Ahdesmäki and Korbinian Strimmer.

Documentation and Installation:

• Help pages and release history.
• Local download of sda source package.
• Licensed under the GNU GPL version 3 (or any later version).

Additional Material:

• Example R script for analyzing Khan et al. (2001) data.
• Example R script for feature selection using Singh et al. (2002) data.

Relevant Publication:

• Ahdesmäki, M., and K. Strimmer. 2010. Feature selection in omics prediction problems using cat scores and false non-discovery rate control. Ann. Appl. Stat. 4: 503-519. (reprint, arXiv:0903.2003)

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