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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: | 1.1.13 |
| Depends: | R (≥ 3.1), ClassDiscovery |
| Imports: | methods, stats, graphics, oompaBase, kernlab, changepoint, cpm |
| Suggests: | MASS, nFactors |
| Published: | 2022-06-30 |
| Author: | Kevin R. Coombes, Min Wang |
| 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 |
| Reference manual: | PCDimension.pdf |
| Vignettes: |
PCDimension |
| Package source: | PCDimension_1.1.13.tar.gz |
| Windows binaries: | r-devel: PCDimension_1.1.13.zip, r-release: PCDimension_1.1.13.zip, r-oldrel: PCDimension_1.1.13.zip |
| macOS binaries: | r-release (arm64): PCDimension_1.1.13.tgz, r-oldrel (arm64): PCDimension_1.1.13.tgz, r-release (x86_64): PCDimension_1.1.13.tgz, r-oldrel (x86_64): PCDimension_1.1.13.tgz |
| Old sources: | PCDimension archive |
| Reverse depends: | Thresher |
| Reverse suggests: | parameters |
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