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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: | 0.9.13 |
Depends: | forecast, ggplot2 |
Imports: | Rsolnp, pracma, signal, zoo, lubridate, crayon, utils, nloptr, BB, GA, dfoptim, pso, FKF, tswge, hypergeo, ltsa |
Suggests: | longmemo, yardstick, testthat, knitr, rmarkdown |
Published: | 2023-08-19 |
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 |
Reference manual: | garma.pdf |
Vignettes: |
Introduction to GARMA models |
Package source: | garma_0.9.13.tar.gz |
Windows binaries: | r-devel: garma_0.9.13.zip, r-release: garma_0.9.13.zip, r-oldrel: garma_0.9.13.zip |
macOS binaries: | r-release (arm64): garma_0.9.13.tgz, r-oldrel (arm64): garma_0.9.13.tgz, r-release (x86_64): garma_0.9.13.tgz, r-oldrel (x86_64): garma_0.9.13.tgz |
Old sources: | garma archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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