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
|Author||Kevin R. Coombes, Min Wang|
|Date of publication||2018-01-09 15:53:01|
|Maintainer||Kevin R. Coombes <[email protected]>|
|License||Apache License (== 2.0)|
|Package repository||View on R-Forge|
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