pfica: Independent Components Analysis Techniques for Functional Data

Performs 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 an 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>.

Version: 0.1.3
Depends: R (≥ 2.10), fda
Imports: expm, whitening
Published: 2023-01-06
DOI: NA
Author: Marc Vidal ORCID iD [aut, cre], Ana Mª Aguilera ORCID iD [aut, ths]
Maintainer: Marc Vidal <marc.vidalbadia at ugent.be>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/m-vidal/pfica
NeedsCompilation: no
CRAN checks: pfica results

Documentation:

Reference manual: pfica.pdf

Downloads:

Package source: pfica_0.1.3.tar.gz
Windows binaries: r-devel: pfica_0.1.3.zip, r-release: pfica_0.1.3.zip, r-oldrel: pfica_0.1.3.zip
macOS binaries: r-release (arm64): pfica_0.1.3.tgz, r-oldrel (arm64): pfica_0.1.3.tgz, r-release (x86_64): pfica_0.1.3.tgz, r-oldrel (x86_64): pfica_0.1.3.tgz
Old sources: pfica archive

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