Package: superpc 1.12

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.

Authors:Eric Bair [aut], Jean-Eudes Dazard [cre, ctb], Rob Tibshirani [ctb]

superpc_1.12.tar.gz
superpc_1.12.zip(r-4.7)superpc_1.12.zip(r-4.6)superpc_1.12.zip(r-4.5)
superpc_1.12.tgz(r-4.6-any)superpc_1.12.tgz(r-4.5-any)
superpc_1.12.tar.gz(r-4.7-any)superpc_1.12.tar.gz(r-4.6-any)
superpc_1.12.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
superpc/json (API)
NEWS

# Install 'superpc' in R:
install.packages('superpc', repos = c('https://jedazard.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/jedazard/superpc/issues

On CRAN:

Conda:

7.42 score 8 stars 2 packages 111 scripts 11k downloads 9 mentions 15 exports 3 dependencies

Last updated from:45faf22b7e. Checks:7 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64NOTE123
source / vignettesOK156
linux-release-x86_64NOTE123
macos-release-arm64NOTE157
macos-oldrel-arm64NOTE175
windows-develNOTE86
windows-releaseNOTE93
windows-oldrelNOTE79
wasm-releaseOK95

Exports:superpc.cvsuperpc.decorrelatesuperpc.fit.to.outcomesuperpc.listfeaturessuperpc.lrtest.curvsuperpc.newssuperpc.plot.lrtestsuperpc.plotcvsuperpc.plotred.lrtestsuperpc.predictsuperpc.predict.redsuperpc.predict.red.cvsuperpc.predictionplotsuperpc.rainbowplotsuperpc.train

Dependencies:latticeMatrixsurvival