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Implements methods to automate the AuerGervini 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>.
Package details 


Author  Kevin R. Coombes, Min Wang 
Bioconductor views  Clustering 
Maintainer  Kevin R. Coombes <[email protected]> 
License  Apache License (== 2.0) 
Version  1.1.9 
URL  http://oompa.rforge.rproject.org/ 
Package repository  View on RForge 
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