garma: Fitting and Forecasting Gegenbauer ARMA Time Series Models

Methods for estimating long memory-seasonal/cyclical Gegenbauer univariate time series processes. See for example (2018) <doi:10.1214/18-STS649>. Refer to the vignette for details of fitting these processes.

Version: 0.9.3
Imports: assertthat, zoo, forecast, lubridate, FKF, signal, pracma, nloptr, Rsolnp, ggplot2, Rdpack (≥ 0.7)
Suggests: longmemo, tidyverse, BB, GA, pso, dfoptim, testthat, knitr, rmarkdown
Published: 2020-08-31
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
In views: TimeSeries
CRAN checks: garma results

Downloads:

Reference manual: garma.pdf
Vignettes: Introduction to GARMA models
Package source: garma_0.9.3.tar.gz
Windows binaries: r-devel: garma_0.9.3.zip, r-release: garma_0.9.3.zip, r-oldrel: garma_0.9.3.zip
macOS binaries: r-release: garma_0.9.3.tgz, r-oldrel: garma_0.9.3.tgz
Old sources: garma archive

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