Package: garma 0.9.23

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-Forge:

4.60 score 2 stars 5 scripts 460 downloads 6 exports 54 dependencies

Last updated 6 months agofrom:ef1f3c2fe7. Checks:8 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKFeb 10 2025
R-4.5-winOKFeb 10 2025
R-4.5-macOKFeb 10 2025
R-4.5-linuxOKFeb 10 2025
R-4.4-winOKFeb 10 2025
R-4.4-macOKFeb 10 2025
R-4.3-winOKFeb 10 2025
R-4.3-macOKFeb 10 2025

Exports:extract_armagarmagarma_ggtsdisplaygg_raw_pgramggbr_semiparagof

Dependencies:clicolorspacecpp11crayoncurlfansifarverforecastfracdiffgenericsggplot2gluegtableisobandjsonlitelabelinglatticelifecyclelmtestlubridatemagrittrMASSMatrixmgcvmunsellnlmenloptrnnetpillarpkgconfigpracmaquadprogquantmodR6RColorBrewerRcppRcppArmadillorlangRsolnpscalessignaltibbletimechangetimeDatetruncnormtseriesTTRurcautf8vctrsviridisLitewithrxtszoo

An introduction to GARMA models

Richard Hunt

Rendered fromintroduction.Rmdusingknitr::rmarkdownon Feb 10 2025.

Last update: 2024-08-23
Started: 2020-05-19