pfica
0.1.3Independent Components Analysis Techniques for Functional Data
Overview
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.
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Health
- OK2026-06-0913 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-06-0812 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
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Code & Tests
- Cyclomatic complexity
- 11.0 median / 14 max
Test coverage
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Functions
4 0 exported
Complexity
11.5 avg / 14 max
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4 nodes / 3 edges
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Package metadata
- First published
- 2020-09-18
- Total releases
- 4 / 6 yrs
- License
- GPL (>= 2) OSI
- Minimum R
- ≥ 2.10
- Download size
- 6.9 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet