Package: garma 1.0.1

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.

Authors:Richard Hunt [aut, cre]

garma_1.0.1.tar.gz
garma_1.0.1.zip(r-4.7)garma_1.0.1.zip(r-4.6)garma_1.0.1.zip(r-4.5)
garma_1.0.1.tgz(r-4.6-any)garma_1.0.1.tgz(r-4.5-any)
garma_1.0.1.tar.gz(r-4.7-any)garma_1.0.1.tar.gz(r-4.6-any)
garma_1.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
garma/json (API)
NEWS

# Install 'garma' in R:
install.packages('garma', repos = c('https://rlph50.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/rlph50/garma/issues

On CRAN:

Conda:

4.00 score 2 stars 5 scripts 355 downloads 6 exports 48 dependencies

Last updated from:9d021a79a6. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK161
source / vignettesOK227
linux-release-x86_64OK168
macos-release-arm64OK197
macos-oldrel-arm64OK276
windows-develOK128
windows-releaseOK115
windows-oldrelOK131
wasm-releaseOK120

Exports:extract_armagarmagarma_ggtsdisplaygg_raw_pgramggbr_semiparagof

Dependencies:clicodetoolscolorspacecpp11crayondigestfarverforecastfracdifffuturefuture.applygenericsggplot2globalsgluegtableisobandlabelinglatticelifecyclelistenvlmtestlubridatemagrittrMASSnlmenloptrnnetnumDerivparallellypracmaR6RColorBrewerRcppRcppArmadillorlangRsolnpS7scalessignaltimechangetimeDatetruncnormurcavctrsviridisLitewithrzoo

An introduction to GARMA models

Richard Hunt

Rendered fromintroduction.Rmdusingknitr::rmarkdownon May 08 2026.

Last update: 2025-03-16
Started: 2020-05-19