Quantify the serial correlation across lags of a given functional time series using an autocorrelation function for functional time series. The autocorrelation function is based on the L2 norm of the lagged covariance operators of the series. Functions are available for estimating the distribution of the autocorrelation function under the assumption of strong functional white noise.
| Version: | 0.1.0 |
| Depends: | R (≥ 3.5.0) |
| Imports: | CompQuadForm, pracma |
| Suggests: | testthat, fields |
| Published: | 2020-01-24 |
| Author: | Guillermo Mestre Marcos [aut, cre], José Portela González [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 |
| In views: | FunctionalData, TimeSeries |
| CRAN checks: | fdaACF results |
| Reference manual: | fdaACF.pdf |
| Package source: | fdaACF_0.1.0.tar.gz |
| Windows binaries: | r-devel: fdaACF_0.1.0.zip, r-release: fdaACF_0.1.0.zip, r-oldrel: fdaACF_0.1.0.zip |
| macOS binaries: | r-release: fdaACF_0.1.0.tgz, r-oldrel: fdaACF_0.1.0.tgz |
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