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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 |
| Reference manual: | pcdpca.pdf |
| 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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These binaries (installable software) and packages are in development.
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