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'))

Peer review:

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

On CRAN:

6 exports 2 stars 1.22 score 54 dependencies 5 scripts 760 downloads

Last updated 5 days agofrom:ef1f3c2fe7. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 13 2024
R-4.5-winOKSep 13 2024
R-4.5-linuxOKSep 13 2024
R-4.4-winOKSep 13 2024
R-4.4-macOKSep 13 2024
R-4.3-winOKSep 13 2024
R-4.3-macOKSep 13 2024

Exports:extract_armagarmagarma_ggtsdisplaygg_raw_pgramggbr_semiparagof

Dependencies:clicolorspacecpp11crayoncurlfansifarverforecastfracdiffgenericsggplot2gluegtableisobandjsonlitelabelinglatticelifecyclelmtestlubridatemagrittrMASSMatrixmgcvmunsellnlmenloptrnnetpillarpkgconfigpracmaquadprogquantmodR6RColorBrewerRcppRcppArmadillorlangRsolnpscalessignaltibbletimechangetimeDatetruncnormtseriesTTRurcautf8vctrsviridisLitewithrxtszoo

An introduction to GARMA models

Richard Hunt

Rendered fromintroduction.Rmdusingknitr::rmarkdownon Sep 13 2024.

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