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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 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
In views: Survival
CRAN checks: superpc results

Documentation:

Reference manual: superpc.pdf

Downloads:

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 (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

Linking:

Please use the canonical form https://CRAN.R-project.org/package=superpc to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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