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-4.4-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

Peer review:

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

On CRAN:

15 exports 6 stars 2.83 score 3 dependencies 2 dependents 9 mentions 69 scripts 3.1k downloads

Last updated 2 years agofrom:b97ad59c5d. Checks:OK: 1 NOTE: 6. Indexed: yes.

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