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], Ana Mª Aguilera [aut, ths]
Maintainer: Marc Vidal <marc.vidalbadia@ugent.be>
Description: 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>.
License: GPL (>= 2)
Depends: R (>= 2.10), fda
Imports: expm, whitening
URL: https://github.com/m-vidal/pfica
Encoding: UTF-8
NeedsCompilation: no
RoxygenNote: 7.2.0
Config/pak/sysreqs: make
Repository: https://marcvidalbadia.r-universe.dev
Date/Publication: 2025-07-26 08:37:54 UTC
RemoteUrl: https://github.com/marcvidalbadia/pfica
RemoteRef: HEAD
RemoteSha: 7ec81446d60b161ffceb017506f1a92e21f732c1
Packaged: 2026-10-04 09:30:32 UTC; root