Quantify the serial correlation across lags of a given functional time series using an autocorrelation function and a partial autocorrelation function for functional time series. The autocorrelation functions are based on the L2 norm of the lagged covariance operators of the series. Functions are available for estimating the distribution of the autocorrelation functions under the assumption of strong functional white noise.
| Version: | 0.2.0 |
| Depends: | R (≥ 3.5.0) |
| Imports: | CompQuadForm, pracma, fda, Matrix, vars |
| Suggests: | testthat, fields |
| Published: | 2020-08-11 |
| Author: | Guillermo Mestre Marcos [aut, cre], José Portela González [aut], Gregory Rice [aut], Antonio Muñoz San Roque [ctb], Estrella Alonso Pérez [ctb] |
| Maintainer: | Guillermo Mestre Marcos <guillermo.mestre at comillas.edu> |
| BugReports: | https://github.com/GMestreM/fdaACF/issues |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://github.com/GMestreM/fdaACF |
| NeedsCompilation: | no |
| Materials: | NEWS |
| In views: | FunctionalData, TimeSeries |
| CRAN checks: | fdaACF results |
| Reference manual: | fdaACF.pdf |
| Package source: | fdaACF_0.2.0.tar.gz |
| Windows binaries: | r-devel: fdaACF_0.2.0.zip, r-release: fdaACF_0.2.0.zip, r-oldrel: fdaACF_0.2.0.zip |
| macOS binaries: | r-release: fdaACF_0.2.0.tgz, r-oldrel: fdaACF_0.2.0.tgz |
| Old sources: | fdaACF archive |
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