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: |
0.9.20 |
Depends: |
forecast, ggplot2 |
Imports: |
Rsolnp, nloptr, pracma, signal, zoo, lubridate, rlang, crayon, utils |
Suggests: |
longmemo, yardstick, testthat (≥ 3.0.0), knitr, rmarkdown |
Published: |
2024-08-23 |
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:
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