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||2018-05-18 01:01:24|
|Maintainer||Kevin R. Coombes <[email protected]>|
|License||Apache License (== 2.0)|
|Package repository||View on R-Forge|
Install the latest version of this package by entering the following in R:
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.