Finding the Number of Significant Principal Components R package PCDimension version 1.1.14 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>. Software Depends: R (>= 4.4), ClassDiscovery Imports: methods, stats, graphics, oompaBase, kernlab, changepoint, cpm Suggests: MASS, nFactors Kevin R. Coombes <krc@silicovore.com> Comprehensive R Archive Network (CRAN) Min Wang [aut], Kevin R. Coombes [aut, cre] Apache License (== 2.0) 2025-04-07 application/tgz https://CRAN.R-project.org/package=PCDimension doi:10.32614/CRAN.package.PCDimension