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 |
Documentation:
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