PCDimension (1.1.9)

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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 .

Maintainer: Kevin R. Coombes
Author(s): Kevin R. Coombes, Min Wang

License: Apache License (== 2.0)

Uses: changepoint, ClassDiscovery, cpm, kernlab, oompaBase, nFactors, MASS
Reverse depends: Thresher

Released 8 months ago.

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