garma: Fitting and Forecasting Gegenbauer ARMA Time Series Models
Methods for estimating univariate long memory-seasonal/cyclical
Gegenbauer time series processes. See for example (2022) <doi:10.1007/s00362-022-01290-3>.
Refer to the vignette for details of fitting these processes.
| Version: |
0.9.23 |
| Depends: |
forecast, ggplot2 |
| Imports: |
Rsolnp, nloptr, pracma, signal, zoo, lubridate, rlang, crayon, utils |
| Suggests: |
longmemo, yardstick, testthat (≥ 3.0.0), knitr, rmarkdown |
| Published: |
2024-09-13 |
| DOI: |
10.32614/CRAN.package.garma |
| 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 |
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=garma
to link to this page.