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: | 1.3.7 |
| Depends: | R (≥ 3.0.2), entropy (≥ 1.2.1), corpcor (≥ 1.6.8), fdrtool (≥ 1.2.15) |
| Imports: | graphics, stats, utils |
| Suggests: | crossval (≥ 1.0.3) |
| Published: | 2015-07-08 |
| Author: | Miika Ahdesmaki, Verena Zuber, Sebastian Gibb, and Korbinian Strimmer |
| Maintainer: | Korbinian Strimmer <strimmerlab at gmail.com> |
| License: | GPL (≥ 3) |
| URL: | http://strimmerlab.org/software/sda/ |
| NeedsCompilation: | no |
| Materials: | NEWS |
| In views: | MachineLearning |
| CRAN checks: | sda results |
| Reference manual: | sda.pdf |
| Package source: | sda_1.3.7.tar.gz |
| Windows binaries: | r-devel: sda_1.3.7.zip, r-release: sda_1.3.7.zip, r-oldrel: sda_1.3.7.zip |
| macOS binaries: | r-release: sda_1.3.7.tgz, r-oldrel: sda_1.3.7.tgz |
| Old sources: | sda archive |
| Reverse depends: | st |
| Reverse imports: | FADA |
| Reverse suggests: | crossval, fscaret, mlr |
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