Package: PCDimension
Version: 1.1.14
Date: 2025-04-07
Title: Finding the Number of Significant Principal Components
Authors@R: c(person(given = "Min", family = "Wang", role = "aut"),
	    person(given = c("Kevin", "R."), family = "Coombes",
                   role = c("aut", "cre"), email = "krc@silicovore.com"))
Description: Implements methods to automate the Auer-Gervini graphical
        Bayesian approach for determining the number of significant
        principal components. Automation uses clustering, change
        points, or simple statistical models to distinguish "long" from
        "short" steps in a graph showing the posterior number of
        components as a function of a prior parameter. See
        <doi:10.1101/237883>.
Depends: R (>= 4.4), ClassDiscovery
Imports: methods, stats, graphics, oompaBase, kernlab, changepoint, cpm
Suggests: MASS, nFactors
License: Apache License (== 2.0)
biocViews: Clustering
URL: http://oompa.r-forge.r-project.org/
NeedsCompilation: no
Packaged: 2026-07-04 01:51:03 UTC; root
Author: Min Wang [aut], Kevin R. Coombes [aut, cre]
Maintainer: Kevin R. Coombes <krc@silicovore.com>
Repository: https://cran.r-universe.dev
Date/Publication: 2025-04-07 22:20:02 UTC
RemoteUrl: https://github.com/cran/PCDimension
RemoteRef: HEAD
RemoteSha: ab3ca5107a95fe4e70c3c9825aa37eea29b58f09