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:

Reference manual:	PCDimension.html , PCDimension.pdf
Vignettes:	PCDimension (source, R code)

Downloads:

Package source:	PCDimension_1.1.14.tar.gz
Windows binaries:	r-devel: PCDimension_1.1.14.zip, r-release: PCDimension_1.1.14.zip, r-oldrel: PCDimension_1.1.14.zip
macOS binaries:	r-release (arm64): PCDimension_1.1.14.tgz, r-oldrel (arm64): PCDimension_1.1.14.tgz, r-release (x86_64): PCDimension_1.1.14.tgz, r-oldrel (x86_64): PCDimension_1.1.14.tgz
Old sources:	PCDimension archive

Reverse dependencies:

Reverse depends:	Thresher
Reverse suggests:	parameters, RPointCloud

Linking:

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