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:

Reference manual:	garma.pdf
Vignettes:	Introduction to GARMA models (source, R code)

Downloads:

Package source:	garma_0.9.23.tar.gz
Windows binaries:	r-devel: garma_0.9.23.zip, r-release: garma_0.9.23.zip, r-oldrel: garma_0.9.23.zip
macOS binaries:	r-release (arm64): garma_0.9.23.tgz, r-oldrel (arm64): garma_0.9.23.tgz, r-release (x86_64): garma_0.9.23.tgz, r-oldrel (x86_64): garma_0.9.23.tgz
Old sources:	garma archive

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