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PCDimension  

Finding the Number of Significant Principal Components
View on CRAN: Click here


Download and install PCDimension package within the R console
Install from CRAN:
install.packages("PCDimension")

Install from Github:
library("remotes")
install_github("cran/PCDimension")

Install by package version:
library("remotes")
install_version("PCDimension", "1.1.15")



Attach the package and use:
library("PCDimension")
Maintained by
Kevin R. Coombes
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2017-12-15
Latest Update: 2025-04-07
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 .
How to cite:
Kevin R. Coombes (2017). PCDimension: Finding the Number of Significant Principal Components. R package version 1.1.15, https://cran.r-project.org/web/packages/PCDimension. Accessed 30 Oct. 2026.
Previous versions and publish date:
(2026-09-25 15:30), 1.1.7 (2017-12-15 10:23), 1.1.8 (2018-01-09 19:26), 1.1.9 (2018-05-18 14:24), 1.1.10 (2019-04-23 18:30), 1.1.11 (2019-05-06 21:00), 1.1.13 (2022-06-30 19:30), 1.1.14 (2025-04-08 00:20)
Other packages that cited PCDimension R package
View PCDimension citation profile
Other R packages that PCDimension depends, imports, suggests or enhances
Complete documentation for PCDimension
Functions, R codes and Examples using the PCDimension R package
Some associated functions: AuerGervini-class . agfun . brokenStick . compare . rndLambdaF . spca-data . 
Some associated R codes: 00-Auer-Gervini.R .  Full PCDimension package functions and examples
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