Package: pcdpca 0.4

Lukasz Kidzinski

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 <arxiv:1612.00040>.

Authors:Lukasz Kidzinski [aut, cre], Neda Jouzdani [aut], Piotr Kokoszka [aut]

pcdpca_0.4.tar.gz
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pcdpca.pdf |pcdpca.html✨
pcdpca/json (API)

Peer review:

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3 exports 0.00 score 50 dependencies 8 scripts 217 downloads

Last updated 7 years agofrom:994e422255. Checks:OK: 1 NOTE: 1. Indexed: yes.

Exports:pcdpcapcdpca.inversepcdpca.scores

Dependencies:ashbitopscliclustercolorspacedeSolvefansifarverfdafdsFNNfreqdomggplot2gluegtablehdrcdeisobandkernlabKernSmoothkslabelinglatticelifecyclelocfitmagrittrMASSMatrixmatrixcalcmclustmgcvmulticoolmunsellmvtnormnlmepcaPPpillarpkgconfigpracmaR6rainbowRColorBrewerRcppRCurlrlangscalestibbleutf8vctrsviridisLitewithr