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pcdpca  

Dynamic Principal Components for Periodically Correlated Functional Time Series
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("pcdpca", "0.4")



Attach the package and use:
library("pcdpca")
Maintained by
Lukasz Kidzinski
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2016-11-27
Latest Update: 2017-09-03
Description:
Method extends multivariate and functional dynamic principal components to periodically correlated multivariate time series. This package allows you to compute true dynamic principal components in the presence of periodicity. We follow implementation guidelines as described in Kidzinski, Kokoszka and Jouzdani (2017), in Principal component analysis of periodically correlated functional time series .
How to cite:
Lukasz Kidzinski (2016). pcdpca: Dynamic Principal Components for Periodically Correlated Functional Time Series. R package version 0.4, https://cran.r-project.org/web/packages/pcdpca. Accessed 22 Dec. 2024.
Previous versions and publish date:
0.2.1 (2016-11-27 00:06)
Other packages that cited pcdpca R package
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Other R packages that pcdpca depends, imports, suggests or enhances
Complete documentation for pcdpca
Functions, R codes and Examples using the pcdpca R package
Some associated functions: pcdpca.inverse . pcdpca . pcdpca.scores . 
Some associated R codes: pc2stat.R . pcdpca.R . pcdpca.inverse.R . pcdpca.scores.R . stat2pc.R .  Full pcdpca package functions and examples
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