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: 1.1.13
Depends: R (≥ 3.1), ClassDiscovery
Imports: methods, stats, graphics, oompaBase, kernlab, changepoint, cpm
Suggests: MASS, nFactors
Published: 2022-06-30
DOI: 10.32614/CRAN.package.PCDimension
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 dependencies:

Reverse depends: Thresher
Reverse suggests: parameters, RPointCloud


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