PCDimension: Finding the Number of Significant Principal Components
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 |
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