Package: pfica 0.1.3

pfica: Independent Components Analysis Techniques for Functional Data

This package includes a set of tools to perform smoothed (and non-smoothed) principal/independent components analysis of functional data. Various functional pre-whitening approaches are implemented as discussed in Vidal and Aguilera (2022) “Novel whitening approaches in functional settings", <doi:10.1002/sta4.516>. Further whitening representations of functional data can be derived in terms of a few principal components, providing a powerful avenue to explore hidden structures in low dimensional settings: see Vidal, Rosso and Aguilera (2021) “Bi-smoothed functional independent component analysis for EEG artifact removal”, <doi:10.3390/math9111243>.

Authors:Marc Vidal [aut, cre], Ana Mª Aguilera [aut, ths]

pfica_0.1.3.tar.gz
pfica_0.1.3.zip(r-4.7-any)pfica_0.1.3.zip(r-4.6-any)pfica_0.1.3.zip(r-4.5-any)
pfica_0.1.3.tgz(r-4.6-any)pfica_0.1.3.tgz(r-4.5-any)
pfica_0.1.3.tar.gz(r-4.7-any)pfica_0.1.3.tar.gz(r-4.6-any)
pfica_0.1.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
pfica/json (API)

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

Bug tracker:https://github.com/m-vidal/pfica/issues

On CRAN:

Conda:

b-splinesfobiicakurtosispenalization

3.00 score 2 stars 3 scripts 277 downloads 4 exports 46 dependencies

Last updated from:7ec81446d6. Checks:7 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develNOTE123
source / vignettesOK152
linux-releaseNOTE159
macos-releaseNOTE191
macos-oldrelNOTE167
windows-develNOTE95
windows-releaseNOTE62
windows-oldrelNOTE77
wasm-releaseOK95

Exports:ffobikffobipspline.kffobiwhiten.fd

Dependencies:ashbitopscliclustercolorspacecorpcorcpp11deSolveexpmfarverfdafdsFNNggplot2gluegtablehdrcdeisobandkernlabKernSmoothkslabelinglatticelifecyclelocfitMASSMatrixmclustmgcvmulticoolmvtnormnlmepcaPPpracmaR6rainbowRColorBrewerRcppRCurlrlangS7scalesvctrsviridisLitewhiteningwithr