pcdpca: Dynamic Principal Components for Periodically Correlated Functional Time Series
Version 0.2.1

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

AuthorLukasz Kidzinski [aut, cre], Neda Jouzdani [aut], Piotr Kokoszka [aut]
Date of publication2016-11-27 00:06:38
MaintainerLukasz Kidzinski <lukasz.kidzinski@stanford.edu>
LicenseGPL-3
Version0.2.1
Package repositoryView on CRAN
InstallationInstall the latest version of this package by entering the following in R:
install.packages("pcdpca")

Getting started

README.md

Popular 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...
See all...

All man pages Function index File listing

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...

Functions

pc2stat Source code
pcdpca Man page Source code
pcdpca.inverse Man page Source code
pcdpca.scores Man page Source code
stat2pc Source code

Files

tests
tests/pc.multivariate.R
tests/stat.R
NAMESPACE
demo
demo/simulation.iid.R
demo/00Index
demo/pm10.R
demo/simulation.ar.R
R
R/pcdpca.scores.R
R/pc2stat.R
R/pcdpca.R
R/pcdpca.inverse.R
R/stat2pc.R
README.md
MD5
DESCRIPTION
man
man/pcdpca.scores.Rd
man/pcdpca.Rd
man/pcdpca.inverse.Rd
pcdpca documentation built on May 19, 2017, 10:40 a.m.

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