| Package |
PCDimension |
| Version |
1.1.14 |
| Date |
2025-04-07 |
| Title |
Finding the Number of Significant Principal ComponentsAuthors@R: c(person(given = "Min", family = "Wang", role = "aut"), person(given = c("Kevin", "R."), family = "Coombes", role = c("aut", "cre"), email = "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 (>= 4.4), 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 |
2026-09-22 18:21:06 UTC; KRC |
| Author |
Min Wang [aut], Kevin R. Coombes [aut, cre] |
| Maintainer |
Kevin R. Coombes <krc@silicovore.com> |
| Built |
R 4.6.1; ; 2026-09-22 18:22:05 UTC; windows |
| User Manual | PCDimension-manual.pdf |
| R CHECK | 00check.log |
| Vignettes |
PCDimension.pdf |
![OOMPA logo](../tuba2.gif")