Finding the Number of Significant Principal Components
Overview
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.
Install
Health
- OK13 OK2026-09-08
- ERROR12 OK, 1 ERROR2026-09-07
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK14 OK2026-03-10
Documentation
- Help pages
- 6
- Examples that run
- 100%5 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 83%
- PCDimension PDF
Downloads
Dependencies
Installing PCDimension adds 8 packages (5 with compiled code, 8.1 MB of source tarballs). Heaviest: mclust 2.7 MB, oompaData 2.1 MB, kernlab 1.0 MB. 1 more not on CRAN is not measured.
Code & Tests
Datasets
People & History
Pick two releases to compare their code metrics.
- 1.1.15Latest
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- 1.1.142025-04-08 · diff ↗
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- 1.1.132022-06-30 · diff ↗
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- RR 4.0.0 released · 2020-04-24
- 1.1.112019-05-06 · diff ↗
- RR 3.6.0 released · 2019-04-26
- 1.1.102019-04-23 · diff ↗
- 1.1.92018-05-18 · diff ↗
- RR 3.5.0 released · 2018-04-23
- 1.1.82018-01-09 · diff ↗
Show 2 earlier eventsHide 2 earlier events
- 1.1.72017-12-15
- RR 3.4.0 released · 2017-04-21
Package metadata
- First published
- 2017-12-15
- Total releases
- 8 / 9 yrs
- License
- Apache License (== 2.0)
- Minimum R
- ≥ 4.4
- Bundled data
- 24 KB / 1 file
- Download size
- 204 KB
Cite
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