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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”, .", "License": "GPL (>= 2)", "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": { "Date": "2026-06-06 10:00:28 UTC", "User": "root" }, "MD5sum": "c4fe8b5d0f8b061db03ad77787426347", "_user": "marcvidalbadia", "_type": "src", "_file": "pfica_0.1.3.tar.gz", "_fileid": "97759df6377a9522a292c9ec14afcda326bcfd407396a80021a0838c34fd3a46", 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