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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.
Getting started
Browse package contents
Package details |
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| Author | Eric Bair [aut], Jean-Eudes Dazard [cre, ctb], Rob Tibshirani [ctb] |
| Maintainer | Jean-Eudes Dazard <jean-eudes.dazard@case.edu> |
| License | GPL (>= 3) | file LICENSE |
| Version | 1.12 |
| URL | http://www-stat.stanford.edu/~tibs/superpc https://github.com/jedazard/superpc |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
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