The classifier is trained using James-Stein-type shrinkage estimators. Variable selection is based on ranking predictors by CAT scores (LDA) or t-scores (DDA). A cutoff is chosen by false non-discovery rate (FNDR) or higher criticism (HC) thresholding.
This approach is particularly suited for high-dimensional classification with correlation among predictors. For details see Zuber and Strimmer (2009) and Ahdesm\"aki and Strimmer (2010).
Typically the functions in this package are applied in three steps:
The accompanying web site (see below) provides example R scripts to illustrate the functionality of this package.
Zuber, V., and K. Strimmer. 2009. Gene ranking and biomarker discovery under correlation. Bioinformatics 25: 2700-2707. Preprint available from http://arxiv.org/abs/0902.0751.
See website: http://strimmerlab.org/software/sda/