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  "Title": "Independent Components Analysis Techniques for Functional Data",
  "Authors@R": "c(person(given = \"Marc\",\nfamily = \"Vidal\",\nrole = c(\"aut\", \"cre\"),\nemail = \"marc.vidalbadia@ugent.be\",\ncomment = c(ORCID = \"0000-0002-1084-3242\")),\nperson(given = c(\"Ana\", \"Mª\"),\nfamily = \"Aguilera\",\nrole = c(\"aut\",\"ths\"),\ncomment = 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\n(and non-smoothed) principal/independent components analysis of\nfunctional data. Various functional pre-whitening approaches\nare implemented as discussed in Vidal and Aguilera (2022)\n“Novel whitening approaches in functional settings\",\n<doi:10.1002/sta4.516>. Further whitening representations of\nfunctional data can be derived in terms of a few principal\ncomponents, providing a powerful avenue to explore hidden\nstructures in low dimensional settings: see Vidal, Rosso and\nAguilera (2021) “Bi-smoothed functional independent component\nanalysis for EEG artifact removal”, <doi:10.3390/math9111243>.",
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  "Date/Publication": "2025-07-26 08:37:54 UTC",
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