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: 0.9.13
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: NA
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:

Reference manual: garma.pdf
Vignettes: Introduction to GARMA models

Downloads:

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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