pcdpca: Dynamic Principal Components for Periodically Correlated Functional Time Series

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Method extends multivariate dynamic principal components to periodically correlated multivariate time series.

Author
Lukasz Kidzinski [aut, cre], Neda Jouzdani [aut], Piotr Kokoszka [aut]
Date of publication
2016-11-27 00:06:38
Maintainer
Lukasz Kidzinski <lukasz.kidzinski@stanford.edu>
License
GPL-3
Version
0.2.1

View on CRAN

Man pages

pcdpca
Compute periodically correlacted DPCA filter coefficients
pcdpca.inverse
Retrieve a process from given scores
pcdpca.scores
Compute periodically correlated DPCA scores, given the...

Files in this package

pcdpca
pcdpca/tests
pcdpca/tests/pc.multivariate.R
pcdpca/tests/stat.R
pcdpca/NAMESPACE
pcdpca/demo
pcdpca/demo/simulation.iid.R
pcdpca/demo/00Index
pcdpca/demo/pm10.R
pcdpca/demo/simulation.ar.R
pcdpca/R
pcdpca/R/pcdpca.scores.R
pcdpca/R/pc2stat.R
pcdpca/R/pcdpca.R
pcdpca/R/pcdpca.inverse.R
pcdpca/R/stat2pc.R
pcdpca/README.md
pcdpca/MD5
pcdpca/DESCRIPTION
pcdpca/man
pcdpca/man/pcdpca.scores.Rd
pcdpca/man/pcdpca.Rd
pcdpca/man/pcdpca.inverse.Rd