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: 0.9.2
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: 2020-08-06
Author: Richard Hunt [aut, cre]
Maintainer: Richard Hunt <maint at>
License: GPL-3
NeedsCompilation: no
Materials: README
CRAN checks: garma results


Reference manual: garma.pdf
Vignettes: Introduction to GARMA models
Package source: garma_0.9.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release: garma_0.9.2.tgz, r-oldrel: garma_0.9.2.tgz


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