Package: pfica
Version: 0.1.3
Title: Independent Components Analysis Techniques for Functional Data
Authors@R: 
  c(person(given = "Marc",
           family = "Vidal",
           role = c("aut", "cre"),
           email = "marc.vidalbadia@ugent.be",
           comment = c(ORCID = "0000-0002-1084-3242")),
    person(given = c("Ana", "Mª"),
           family = "Aguilera",
           role = c("aut","ths"),
           comment = c(ORCID = "0000-0003-2425-6716")))
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>
Description: 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>.  
License: GPL (>= 2)
Depends: R (>= 2.10), fda
Imports: expm, whitening
URL: https://github.com/m-vidal/pfica
Encoding: UTF-8
NeedsCompilation: no
Repository: CRAN
RoxygenNote: 7.2.0
Packaged: 2023-01-05 16:30:04 UTC; marc
Date/Publication: 2023-01-06 09:40:08 UTC
Built: R 4.6.0; ; 2026-04-25 13:15:09 UTC; unix
