Package: garma 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.

Authors:Richard Hunt [aut, cre]

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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:

4.70 score 2 stars 5 scripts 441 downloads 6 exports 54 dependencies

Last updated 17 days agofrom:d8d30fe7fc. Checks:9 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 16 2025
R-4.5-winOKMar 16 2025
R-4.5-macOKMar 16 2025
R-4.5-linuxOKMar 16 2025
R-4.4-winOKMar 16 2025
R-4.4-macOKMar 16 2025
R-4.4-linuxOKMar 16 2025
R-4.3-winOKMar 16 2025
R-4.3-macOKMar 16 2025

Exports:extract_armagarmagarma_ggtsdisplaygg_raw_pgramggbr_semiparagof

Dependencies:clicolorspacecpp11crayoncurlfansifarverforecastfracdiffgenericsggplot2gluegtableisobandjsonlitelabelinglatticelifecyclelmtestlubridatemagrittrMASSMatrixmgcvmunsellnlmenloptrnnetpillarpkgconfigpracmaquadprogquantmodR6RColorBrewerRcppRcppArmadillorlangRsolnpscalessignaltibbletimechangetimeDatetruncnormtseriesTTRurcautf8vctrsviridisLitewithrxtszoo

An introduction to GARMA models

Richard Hunt

Rendered fromintroduction.Rmdusingknitr::rmarkdownon Mar 16 2025.

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