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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
Package details 


Author  Kevin R. Coombes, Min Wang 
Bioconductor views  Clustering 
Date of publication  20180109 15:53:01 
Maintainer  Kevin R. Coombes <[email protected]> 
License  Apache License (== 2.0) 
Version  1.1.8 
URL  http://oompa.rforge.rproject.org/ 
Package repository  View on RForge 
Installation 
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