Methods for estimating univariate long memory-seasonal/cyclical Gegenbauer time series processes. See for example (2018) <doi:10.1214/18-STS649>. Refer to the vignette for details of fitting these processes.
| Version: | 0.9.6 |
| Depends: | forecast |
| Imports: | Rsolnp, ggplot2, pracma, signal, zoo, lubridate, FKF, nloptr, crayon, utils |
| Suggests: | longmemo, yardstick, tidyverse, BB, GA, pso, dfoptim, testthat, knitr, rmarkdown |
| Published: | 2020-10-29 |
| Author: | Richard Hunt [aut, cre] |
| Maintainer: | Richard Hunt <maint at huntemail.id.au> |
| License: | GPL-3 |
| URL: | https://github.com/rlph50/garma |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| In views: | TimeSeries |
| CRAN checks: | garma results |
| Reference manual: | garma.pdf |
| Vignettes: |
Introduction to GARMA models |
| Package source: | garma_0.9.6.tar.gz |
| Windows binaries: | r-devel: garma_0.9.6.zip, r-release: garma_0.9.6.zip, r-oldrel: garma_0.9.6.zip |
| macOS binaries: | r-release: garma_0.9.6.tgz, r-oldrel: garma_0.9.6.tgz |
| Old sources: | garma archive |
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