PCDimension
Finding the Number of Significant Principal Components
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>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.1.14 |
rolling linux/jammy R-4.5 | PCDimension_1.1.14.tar.gz |
200.2 KiB |
1.1.14 |
rolling linux/noble R-4.5 | PCDimension_1.1.14.tar.gz |
200.2 KiB |
1.1.14 |
rolling source/ R- | PCDimension_1.1.14.tar.gz |
200.2 KiB |
1.1.14 |
latest linux/jammy R-4.5 | PCDimension_1.1.14.tar.gz |
200.2 KiB |
1.1.14 |
latest linux/noble R-4.5 | PCDimension_1.1.14.tar.gz |
200.2 KiB |
1.1.14 |
latest source/ R- | PCDimension_1.1.14.tar.gz |
200.2 KiB |
1.1.14 |
2026-04-26 source/ R- | PCDimension_1.1.14.tar.gz |
200.2 KiB |
1.1.14 |
2026-04-23 source/ R- | PCDimension_1.1.14.tar.gz |
200.2 KiB |
1.1.14 |
2025-04-20 source/ R- | PCDimension_1.1.14.tar.gz |
200.2 KiB |