pcdpca: Dynamic Principal Components for Periodically Correlated Functional Time Series

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 <doi:10.48550/arXiv.1612.00040>.

Version:	0.4
Depends:	R (≥ 3.3.1)
Imports:	freqdom, fda
Published:	2017-09-03
DOI:	10.32614/CRAN.package.pcdpca
Author:	Lukasz Kidzinski [aut, cre], Neda Jouzdani [aut], Piotr Kokoszka [aut]
Maintainer:	Lukasz Kidzinski <lukasz.kidzinski at stanford.edu>
License:	GPL-3
NeedsCompilation:	no
Materials:	README
In views:	FunctionalData, TimeSeries
CRAN checks:	pcdpca results

Documentation:

Reference manual:

pcdpca.pdf

Downloads:

Package source:	pcdpca_0.4.tar.gz
Windows binaries:	r-devel: pcdpca_0.4.zip, r-release: pcdpca_0.4.zip, r-oldrel: pcdpca_0.4.zip
macOS binaries:	r-release (arm64): pcdpca_0.4.tgz, r-oldrel (arm64): pcdpca_0.4.tgz, r-release (x86_64): pcdpca_0.4.tgz, r-oldrel (x86_64): pcdpca_0.4.tgz
Old sources:	pcdpca archive

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