MFPCA: Multivariate Functional Principal Component Analysis for Data Observed on Different Dimensional Domains

Calculate a multivariate functional principal component analysis for data observed on different dimensional domains. The estimation algorithm relies on univariate basis expansions for each element of the multivariate functional data (Happ & Greven, 2018) <doi:10.1080/01621459.2016.1273115>. Multivariate and univariate functional data objects are represented by S4 classes for this type of data implemented in the package 'funData'. For more details on the general concepts of both packages and a case study, see Happ (2018) <arXiv:1707.02129>.

Version: 1.3
Depends: R (≥ 3.2.0), funData (≥ 1.2)
Imports: abind, foreach, irlba, Matrix, methods, mgcv, plyr, stats
Suggests: covr, fda, testthat
Published: 2018-08-13
Author: Clara Happ ORCID iD [aut, cre]
Maintainer: Clara Happ <clara.happ at>
License: GPL-2
NeedsCompilation: yes
SystemRequirements: libfftw3 (>= 3.3.4)
Citation: MFPCA citation info
Materials: README NEWS
CRAN checks: MFPCA results


Reference manual: MFPCA.pdf
Package source: MFPCA_1.3.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X binaries: r-release: MFPCA_1.3.tgz, r-oldrel: MFPCA_1.3.tgz
Old sources: MFPCA archive


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