superpc: Supervised Principal Components

Does prediction in the case of a censored survival outcome, or a regression outcome, using the "supervised principal component" approach. 'Superpc' is especially useful for high-dimensional data when the number of features p dominates the number of samples n (p >> n paradigm), as generated, for instance, by high-throughput technologies.

Version: 1.12
Depends: R (≥ 3.5.0)
Imports: survival, stats, graphics, grDevices
Published: 2020-10-19
Author: Eric Bair [aut], Jean-Eudes Dazard [cre, ctb], Rob Tibshirani [ctb]
Maintainer: Jean-Eudes Dazard <jean-eudes.dazard at case.edu>
License: GPL (≥ 3) | file LICENSE
URL: http://www-stat.stanford.edu/~tibs/superpc, https://github.com/jedazard/superpc
NeedsCompilation: no
Citation: superpc citation info
Materials: README NEWS
CRAN checks: superpc results

Downloads:

Reference manual: superpc.pdf
Package source: superpc_1.12.tar.gz
Windows binaries: r-devel: superpc_1.12.zip, r-release: superpc_1.12.zip, r-oldrel: superpc_1.12.zip
macOS binaries: r-release: superpc_1.12.tgz, r-oldrel: superpc_1.12.tgz
Old sources: superpc archive

Reverse dependencies:

Reverse suggests: caret, fscaret

Linking:

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