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: |
1.3-9 |
| Depends: |
R (≥ 3.2.0), funData (≥ 1.3-4) |
| Imports: |
abind, foreach, irlba, Matrix, methods, mgcv (≥ 1.8-33), plyr, stats |
| Suggests: |
covr, fda, testthat (≥ 2.0.0) |
| Published: |
2021-10-17 |
| Author: |
Clara Happ-Kurz 
[aut, cre] |
| Maintainer: |
Clara Happ-Kurz <chk_R at gmx.de> |
| License: |
GPL-2 |
| URL: |
https://github.com/ClaraHapp/MFPCA |
| NeedsCompilation: |
yes |
| SystemRequirements: |
libfftw3 (>= 3.3.4) |
| Citation: |
MFPCA citation info |
| Materials: |
README NEWS |
| In views: |
FunctionalData |
| CRAN checks: |
MFPCA results |
Documentation:
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