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.

Getting started

Package details

AuthorEric Bair [aut], Jean-Eudes Dazard [cre, ctb], Rob Tibshirani [ctb]
MaintainerJean-Eudes Dazard <>
LicenseGPL (>= 3) | file LICENSE
Package repositoryView on CRAN
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superpc documentation built on Oct. 24, 2020, 1:07 a.m.