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>.

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Description file content

Package
MFPCA
Type
Package
Title
Multivariate Functional Principal Component Analysis for Data Observed on Different Dimensional Domains
Version
1.2-2
Date
2018-05-23
Maintainer
Clara Happ
Description
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) . 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) .
License
GPL-2
Imports
abind, foreach, irlba, Matrix, methods, mgcv, plyr, stats
Depends
R (>= 3.2.0), funData (>= 1.2)
Suggests
covr, testthat
NeedsCompilation
yes
SystemRequirements
libfftw3 (>= 3.3.4)
RoxygenNote
6.0.1
Packaged
2018-05-24 09:45:20 UTC; happ
Author
Clara Happ [aut, cre] ()
Repository
CRAN
Date/Publication
2018-05-25 18:00:14

install.packages('MFPCA')

1.2-2

27 days ago

Clara Happ

GPL-2

Depends on

R (>= 3.2.0), funData (>= 1.2)

Imports

abind, foreach, irlba, Matrix, methods, mgcv, plyr, stats

Suggests

covr, testthat

Discussions