_            _    _        _         _
      /\ \         /\ \ /\ \     /\_\      / /\
      \_\ \       /  \ \\ \ \   / / /     / /  \
      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
   / / /  \/_// / /   / / / \ \ \        \ \ \
  / / /      / / /   / / /   \ \ \   _    \ \ \
 / / /      / / /___/ / /     \ \ \ /_/\__/ / /
/_/ /      / / /____\/ /       \ \_\\ \/___/ /
\_\/       \/_________/         \/_/ \_____\/
r-pcdimension 1.1.14
Propagated dependencies: r-oompabase@3.2.9 r-kernlab@0.9-33 r-cpm@2.3 r-classdiscovery@3.4.5 r-changepoint@2.3
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: http://oompa.r-forge.r-project.org/
Licenses: ASL 2.0
Synopsis: Finding the Number of Significant Principal Components
Description:

This package 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>.

Total results: 1