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.

AuthorMiika Ahdesmaki, Verena Zuber, Sebastian Gibb, and Korbinian Strimmer
Date of publication2015-07-08 16:28:41
MaintainerKorbinian Strimmer <strimmerlab@gmail.com>
LicenseGPL (>= 3)
Version1.3.7
http://strimmerlab.org/software/sda/

View on CRAN

Files

sda
sda/inst
sda/inst/doc
sda/inst/doc/index.html
sda/inst/doc/sda-khan-data.R
sda/inst/doc/sda-singh-data.R
sda/NAMESPACE
sda/NEWS
sda/data
sda/data/khan2001.rda
sda/data/singh2002.rda
sda/data/datalist
sda/R
sda/R/predict.sda.R sda/R/catscore.R sda/R/sda.ranking.R sda/R/centroids.R sda/R/sda.R
sda/MD5
sda/DESCRIPTION
sda/man
sda/man/sda.ranking.Rd sda/man/centroids.Rd sda/man/catscore.Rd sda/man/khan2001.Rd sda/man/sda-internal.Rd sda/man/sda.Rd sda/man/singh2002.Rd sda/man/sda.package.Rd sda/man/predict.sda.Rd

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