Package: superpc 1.12

Jean-Eudes Dazard

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-4.5)superpc_1.12.zip(r-4.4)superpc_1.12.zip(r-4.3)
superpc_1.12.tgz(r-4.5-any)superpc_1.12.tgz(r-4.4-any)superpc_1.12.tgz(r-4.3-any)
superpc_1.12.tar.gz(r-4.5-noble)superpc_1.12.tar.gz(r-4.4-noble)
superpc_1.12.tgz(r-4.4-emscripten)superpc_1.12.tgz(r-4.3-emscripten)
superpc.pdf |superpc.html✨
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:

6.72 score 6 stars 2 packages 80 scripts 4.0k downloads 9 mentions 15 exports 3 dependencies

Last updated 3 years agofrom:b97ad59c5d. Checks:1 OK, 7 NOTE. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKJan 30 2025
R-4.5-winNOTEJan 30 2025
R-4.5-macNOTEJan 30 2025
R-4.5-linuxNOTEJan 30 2025
R-4.4-winNOTEJan 30 2025
R-4.4-macNOTEJan 30 2025
R-4.3-winNOTEJan 30 2025
R-4.3-macNOTEJan 30 2025

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