Package: superpc
Type: Package
Title: Supervised Principal Components
Version: 1.12
Date: 2020-10-19
Authors@R: c(person("Eric", "Bair",
                    role = "aut",
                    email = "ebair@email.unc.edu"),
             person("Jean-Eudes", "Dazard",
                    role = c("cre", "ctb"),
                    email = "jean-eudes.dazard@case.edu"),
             person("Rob", "Tibshirani",
                    role = "ctb",
	               email = "tibs@stanford.edu"))
Author: Eric Bair [aut],
  Jean-Eudes Dazard [cre, ctb],
  Rob Tibshirani [ctb]
Maintainer: Jean-Eudes Dazard <jean-eudes.dazard@case.edu>
Description: 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.
Depends: R (>= 3.5.0)
Imports: survival, stats, graphics, grDevices
NeedsCompilation: no
URL: http://www-stat.stanford.edu/~tibs/superpc,
        https://github.com/jedazard/superpc
Repository: CRAN
Date/Publication: 2020-10-19 22:10:03 UTC
License: GPL (>= 3) | file LICENSE
Archs: i386, x64
Packaged: 2020-10-19 18:31:57 UTC; JE D
Built: R 4.6.0; ; 2026-04-27 18:19:19 UTC; unix
