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.
| Version: | 1.12 |
| Depends: | R (≥ 3.5.0) |
| Imports: | survival, stats, graphics, grDevices |
| Published: | 2020-10-19 |
| Author: | Eric Bair [aut], Jean-Eudes Dazard [cre, ctb], Rob Tibshirani [ctb] |
| Maintainer: | Jean-Eudes Dazard <jean-eudes.dazard at case.edu> |
| License: | GPL (≥ 3) | file LICENSE |
| URL: | http://www-stat.stanford.edu/~tibs/superpc, https://github.com/jedazard/superpc |
| NeedsCompilation: | no |
| Citation: | superpc citation info |
| Materials: | README NEWS |
| In views: | Survival |
| CRAN checks: | superpc results |
| Reference manual: | superpc.pdf |
| Package source: | superpc_1.12.tar.gz |
| Windows binaries: | r-devel: superpc_1.12.zip, r-release: superpc_1.12.zip, r-oldrel: superpc_1.12.zip |
| macOS binaries: | r-release (arm64): superpc_1.12.tgz, r-oldrel (arm64): superpc_1.12.tgz, r-release (x86_64): superpc_1.12.tgz, r-oldrel (x86_64): superpc_1.12.tgz |
| Old sources: | superpc archive |
| Reverse imports: | MetabolicSurv |
| Reverse suggests: | caret, fscaret |
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