Package: MFPCA 1.3-9
Clara Happ-Kurz
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>.
Authors:
MFPCA_1.3-9.tar.gz
MFPCA_1.3-9.zip(r-4.5)MFPCA_1.3-9.zip(r-4.4)MFPCA_1.3-9.zip(r-4.3)
MFPCA_1.3-9.tgz(r-4.4-x86_64)MFPCA_1.3-9.tgz(r-4.4-arm64)MFPCA_1.3-9.tgz(r-4.3-x86_64)MFPCA_1.3-9.tgz(r-4.3-arm64)
MFPCA_1.3-9.tar.gz(r-4.5-noble)MFPCA_1.3-9.tar.gz(r-4.4-noble)
MFPCA_1.3-9.tgz(r-4.4-emscripten)MFPCA_1.3-9.tgz(r-4.3-emscripten)
MFPCA.pdf |MFPCA.html✨
MFPCA/json (API)
NEWS
| # Install 'MFPCA' in R: |
| install.packages('MFPCA', repos = c('https://clarahapp.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/clarahapp/mfpca/issues
Last updated 3 years agofrom:d062f86d57. Checks:OK: 7 NOTE: 2. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 26 2024 |
| R-4.5-win-x86_64 | NOTE | Oct 26 2024 |
| R-4.5-linux-x86_64 | NOTE | Oct 26 2024 |
| R-4.4-win-x86_64 | OK | Oct 26 2024 |
| R-4.4-mac-x86_64 | OK | Oct 26 2024 |
| R-4.4-mac-aarch64 | OK | Oct 26 2024 |
| R-4.3-win-x86_64 | OK | Oct 26 2024 |
| R-4.3-mac-x86_64 | OK | Oct 26 2024 |
| R-4.3-mac-aarch64 | OK | Oct 26 2024 |
Exports:FCP_TPAMFPCAPACEscoreplotttvUMPCAunivDecompunivExpansion
Dependencies:abindcodetoolsdotCall64fieldsforeachfunDatairlbaiteratorslatticemapsMatrixmgcvnlmeplyrRcppspamviridisLite
