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
DOI: 10.32614/CRAN.package.superpc
Author: Eric Bair [aut], Jean-Eudes Dazard [cre, ctb], Rob Tibshirani [ctb]
Maintainer: Jean-Eudes Dazard <jean-eudes.dazard at>
License: GPL (≥ 3) | file LICENSE
NeedsCompilation: no
Citation: superpc citation info
Materials: README NEWS
In views: Survival
CRAN checks: superpc results


Reference manual: superpc.pdf


Package source: superpc_1.12.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): superpc_1.12.tgz, r-oldrel (arm64): superpc_1.12.tgz, r-release (x86_64): superpc_1.12.tgz, r-oldrel (x86_64): superpc_1.12.tgz
Old sources: superpc archive

Reverse dependencies:

Reverse imports: MetabolicSurv, MicrobiomeSurv
Reverse suggests: caret, flowml, fscaret, gspcr


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