sda: Shrinkage Discriminant Analysis and CAT Score Variable Selection , This package provides an efficient framework for high-dimensional linear and diagonal discriminant analysis with variable selection. The classifier is trained using James-Stein-type shrinkage estimators and predictor variables are ranked using CAT scores (correlation-adjusted t-scores). Variable selection error is controlled using false non-discovery rates or higher criticism scores. (Source: http://cran.r-project.org/web/packages)
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References in zbMATH (referenced in 3 articles )
Showing results 1 to 3 of 3.
- Wu, Yufei; Yu, Guan: Weighted linear programming discriminant analysis for high-dimensional binary classification (2020)
- Donoho, David; Jin, Jiashun: Higher criticism for large-scale inference, especially for rare and weak effects (2015)
- Ahdesmäki, Miika; Strimmer, Korbinian: Feature selection in omics prediction problems using cat scores and false nondiscovery rate control (2010)