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PCDimension

1.1.15Package record

Finding the Number of Significant Principal Components

1packages dependsuggested by 2
16.3Kdownloads / year
87.2%test coverage
13/13flavors OK

Overview

About

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

CRAN checks
13OK
Slowest check: 2.1 min · r-release-macos-x86_64
Status changes
  • OK13 OK2026-09-08
  • ERROR12 OK, 1 ERROR2026-09-07
  • OK13 OK2026-08-05
  • NOTE12 OK, 1 NOTE2026-08-01
  • OK14 OK2026-03-10
Advisories

none known

Documentation

In release 1.1.15
READMENoVignettesYes · 1 · dynamicNEWSYes · 33% structuredCitation fileNoWebsiteYes
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%
Vignettes (1)

Downloads

16.3K
CRAN downloads in the past year
Rank #3,030 · ~45/day · ~1.4K/mo
Daily download trend is not available in this view yet.
2K30 days
4.3K90 days
16.3K1 year
Compare downloads with other packages →
Also on137 r2u

Dependencies

Declared dependencies
6 external dependencies (excludes base and recommended)

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.

Depends (2)
Imports (7)
methodsstatsgraphicsoompaBasekernlabchangepointcpm
LinkingTo (0)
none
Suggests (2)
Enhances (0)
none
Reverse dependencies
1direct
5indirect
Depends 1
Suggested by 2
Depends, Imports or LinkingTo, most downloaded first
Suggests, most downloaded first

Datasets

People & History

People (2)
Author, Maintainer since 1.1.7 (2017)
Author
Listed in earlier versions (1)
Coombes
no longer listed · 1.1.14 to 1.1.15
Releases (8)

Pick two releases to compare their code metrics.

  • 1.1.15Latest
    record
    2026-09-25 · current release · diff ↗
  • R
    R 4.6.0 released · 2026-04-24
  • R
    R 4.5.0 released · 2025-04-11
  • 1.1.14
    record
    2025-04-08 · diff ↗
  • R
    R 4.4.0 released · 2024-04-24
  • R
    R 4.3.0 released · 2023-04-21
  • 1.1.13
    record
    2022-06-30 · diff ↗
  • R
    R 4.2.0 released · 2022-04-22
  • R
    R 4.1.0 released · 2021-05-18
  • R
    R 4.0.0 released · 2020-04-24
  • 1.1.11
    record
    2019-05-06 · diff ↗
  • R
    R 3.6.0 released · 2019-04-26
  • 1.1.10
    record
    2019-04-23 · diff ↗
  • 1.1.9
    record
    2018-05-18 · diff ↗
  • R
    R 3.5.0 released · 2018-04-23
  • 1.1.8
    record
    2018-01-09 · diff ↗
Show 2 earlier eventsHide 2 earlier events
  • 1.1.7
    record
    2017-12-15
  • R
    R 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

Cite this package

Run in R for the authors' preferred citation:

citation("PCDimension")
Coombes, K. R., & Wang, M. (2026). PCDimension: Finding the Number of Significant Principal Components (Version 1.1.15) [Computer software]. https://doi.org/10.32614/CRAN.package.PCDimension

This package has no citation file, so citation() returns this entry built from DESCRIPTION.

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

APA

Balamuta, J. J. (2026). R Observatory: Metrics for PCDimension version 1.1.15 [Data set]. HJJB, LLC. Data release v2026-10-18. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-18, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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