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>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.3 |
rolling linux/jammy R-4.5 | pfica_0.1.3.tar.gz |
44.5 KiB |
0.1.3 |
rolling linux/noble R-4.5 | pfica_0.1.3.tar.gz |
44.4 KiB |
0.1.3 |
rolling source/ R- | pfica_0.1.3.tar.gz |
6.9 KiB |
0.1.3 |
latest linux/jammy R-4.5 | pfica_0.1.3.tar.gz |
44.5 KiB |
0.1.3 |
latest linux/noble R-4.5 | pfica_0.1.3.tar.gz |
44.4 KiB |
0.1.3 |
latest source/ R- | pfica_0.1.3.tar.gz |
6.9 KiB |
0.1.3 |
2026-04-26 source/ R- | pfica_0.1.3.tar.gz |
6.9 KiB |
0.1.3 |
2026-04-23 source/ R- | pfica_0.1.3.tar.gz |
6.9 KiB |
0.1.3 |
2026-04-09 windows/windows R-4.5 | pfica_0.1.3.zip |
46.8 KiB |
0.1.3 |
2025-04-20 source/ R- | pfica_0.1.3.tar.gz |
6.9 KiB |