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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>.
Getting started
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Package details |
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| Author | Marc Vidal [aut, cre] (<https://orcid.org/0000-0002-1084-3242>), Ana Mª Aguilera [aut, ths] (<https://orcid.org/0000-0003-2425-6716>) |
| Maintainer | Marc Vidal <marc.vidalbadia@ugent.be> |
| License | GPL (>= 2) |
| Version | 0.1.3 |
| URL | https://github.com/m-vidal/pfica |
| Package repository | View on CRAN |
| Installation |
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