sda: Shrinkage Discriminant Analysis and CAT Score Variable Selection

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 correlation-adjusted t-scores (CAT scores). Variable selection error is controlled using false non-discovery rates or higher criticism.

Version: 1.3.8
Depends: R (≥ 3.0.2), entropy (≥ 1.3.1), corpcor (≥ 1.6.10), fdrtool (≥ 1.2.17)
Imports: graphics, stats, utils
Suggests: crossval
Enhances: care
Published: 2021-11-21
Author: Miika Ahdesmaki, Verena Zuber, Sebastian Gibb, and Korbinian Strimmer
Maintainer: Korbinian Strimmer <strimmerlab at gmail.com>
License: GPL (≥ 3)
URL: https://strimmerlab.github.io/software/sda/
NeedsCompilation: no
Materials: NEWS
In views: MachineLearning
CRAN checks: sda results

Documentation:

Reference manual: sda.pdf

Downloads:

Package source: sda_1.3.8.tar.gz
Windows binaries: r-devel: sda_1.3.8.zip, r-release: sda_1.3.8.zip, r-oldrel: sda_1.3.8.zip
macOS binaries: r-release (arm64): sda_1.3.8.tgz, r-release (x86_64): sda_1.3.8.tgz, r-oldrel: sda_1.3.8.tgz
Old sources: sda archive

Reverse dependencies:

Reverse depends: st
Reverse imports: FADA
Reverse suggests: crossval, discrim, fscaret, mlr
Reverse enhances: care

Linking:

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