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>.
Version: 1.1.14 Depends: R (≥ 4.4), ClassDiscovery Imports: methods, stats, graphics, oompaBase, kernlab, changepoint, cpm Suggests: MASS, nFactors Published: 2025-04-07 DOI: 10.32614/CRAN.package.PCDimension Author: Min Wang [aut], Kevin R. Coombes [aut, cre] Maintainer: Kevin R. Coombes <krc at silicovore.com> License: Apache License (== 2.0) URL: http://oompa.r-forge.r-project.org/ NeedsCompilation: no Materials: NEWS CRAN checks: PCDimension results Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=PCDimension to link to this page.
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