Fitting and Forecasting Gegenbauer ARMA Time Series Models [R package garma version 0.9.24]

Richard Hunt

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: 0.9.24

Depends: forecast, ggplot2

Imports: Rsolnp, nloptr, pracma, signal, zoo, lubridate, rlang, crayon, utils

Suggests: longmemo, yardstick, testthat (≥ 3.0.0), knitr, rmarkdown

Published: 2025年03月16日

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:

Reference manual: garma.html , garma.pdf

Vignettes: Introduction to GARMA models (source, R code)

Downloads:

Package source: garma_0.9.24.tar.gz

Windows binaries: r-devel: garma_0.9.24.zip, r-release: garma_0.9.24.zip, r-oldrel: garma_0.9.24.zip

macOS binaries: r-release (arm64): garma_0.9.24.tgz, r-oldrel (arm64): garma_0.9.24.tgz, r-release (x86_64): garma_0.9.24.tgz, r-oldrel (x86_64): garma_0.9.24.tgz

Old sources: garma archive

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