Package: PCDimension
Version: 1.1.11
Date: 2019-05-06
Title: Finding the Number of Significant Principal Components
Author: Kevin R. Coombes, Min Wang
Maintainer: Kevin R. Coombes <krc@silicovore.com>
Description: 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 <doi:10.1101/237883>.
Depends: R (>= 3.1), ClassDiscovery
Imports: methods, stats, graphics, oompaBase, kernlab, changepoint, cpm
Suggests: MASS, nFactors
License: Apache License (== 2.0)
biocViews: Clustering
URL: http://oompa.r-forge.r-project.org/
NeedsCompilation: no
Packaged: 2019-05-06 17:51:47 UTC; Kevin Coombes
Repository: CRAN
Date/Publication: 2019-05-06 19:00:06 UTC
Built: R 4.0.2; ; 2020-07-16 01:50:26 UTC; unix
