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-Kurz (2020) <doi:10.18637/jss.v093.i05>.

Version: 1.3-10
Depends: R (≥ 3.2.0), funData (≥ 1.3-4)
Imports: abind, foreach, irlba, Matrix (≥ 1.5-0), methods, mgcv (≥ 1.8-33), plyr, stats
Suggests: covr, fda, testthat (≥ 2.0.0)
Published: 2022-09-15
DOI: 10.32614/CRAN.package.MFPCA
Author: Clara Happ-Kurz ORCID iD [aut, cre]
Maintainer: Clara Happ-Kurz <chk_R at>
License: GPL-2
NeedsCompilation: yes
SystemRequirements: libfftw3 (>= 3.3.4)
Citation: MFPCA citation info
Materials: README NEWS
In views: FunctionalData
CRAN checks: MFPCA results


Reference manual: MFPCA.pdf


Package source: MFPCA_1.3-10.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): MFPCA_1.3-10.tgz, r-oldrel (arm64): MFPCA_1.3-10.tgz, r-release (x86_64): MFPCA_1.3-10.tgz, r-oldrel (x86_64): MFPCA_1.3-10.tgz
Old sources: MFPCA archive

Reverse dependencies:

Reverse imports: FADPclust, MJMbamlss, multifamm, squat
Reverse suggests: gmfamm


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