Package: funpca 9.0

funpca: Functional Principal Component Analysis

Functional principal component analysis under the Linear Mixed Models representation of smoothing splines. The method utilizes the Demmler-Reinsch basis and assumes error independence. For more details see: F. Rosales (2016) <https://ediss.uni-goettingen.de/handle/11858/00-1735-0000-0028-87F9-6>.

Authors:Francisco Rosales [aut, cph, cre], Tatyana Krivobokova [con, ths]

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funpca.pdf |funpca.html✨
funpca/json (API)

# Install 'funpca' in R:
install.packages('funpca', repos = c('https://lfrm.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 139 downloads 5 exports 49 dependencies

Last updated 1 years agofrom:92ab27c7fa. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 05 2024
R-4.5-winOKNov 05 2024
R-4.5-linuxOKNov 05 2024
R-4.4-winOKNov 05 2024
R-4.4-macOKNov 05 2024
R-4.3-winOKNov 05 2024
R-4.3-macOKNov 05 2024

Exports:funpcafunpcaEstplot.funpcaprint.funpcasummary.funpca

Dependencies:ashbitopsBrobdingnagcliclustercolorspacedeSolvefansifarverfdafdsFNNggplot2gluegtablehdrcdeisobandkernlabKernSmoothkslabelinglatticelifecyclelocfitmagrittrMASSMatrixmclustmgcvmulticoolmunsellmvtnormnlmepcaPPpillarpkgconfigpracmaR6rainbowRColorBrewerRcppRCurlrlangscalestibbleutf8vctrsviridisLitewithr