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

# 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:

4.00 score 2 stars 9 scripts 515 downloads 6 exports 48 dependencies

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

Target	Result	Time
linux-devel	OK	144
source / vignettes	OK	191
linux-release	OK	135
macos-release	OK	206
macos-oldrel	OK	204
windows-devel	OK	118
windows-release	OK	109
windows-oldrel	OK	104
wasm-release	OK	108

Exports:extract_arma garma garma_ggtsdisplay gg_raw_pgram ggbr_semipara gof

Dependencies:cli codetools colorspace cpp11 crayon digest farver forecast fracdiff future future.apply generics ggplot2 globals glue gtable isoband labeling lattice lifecycle listenv lmtest lubridate magrittr MASS nlme nloptr nnet numDeriv parallelly pracma R6 RColorBrewer Rcpp RcppArmadillo rlang Rsolnp S7 scales signal timechange timeDate truncnorm urca vctrs viridisLite withr zoo

An introduction to GARMA models

Introduction to long memory models and GARMA models. | Technical details of the model. | Fitting a short memory model | Fitting a GARMA model. | References

Rendered fromintroduction.Rmdusingknitr::rmarkdown

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

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
AIC for model	AIC.garma_model
ggplot of the Forecasts of the model.	autoplot.garma_model
Model Coefficients	coef.garma_model
Extract underlying ARMA process.	extract_arma
Return the fitted values for a GARMA forecast.	fitted.garma_forecast
Extract fitted values	fitted.garma_model
Forecast future values.	forecast.garma_model
garma: A package for estimating and foreasting Gegenbauer time series models.	garma-package garma
ggtsdisplay of underlying ARMA process.	garma_ggtsdisplay
Display raw periodogram	gg_raw_pgram
Extract semiparametric estimates of the Gegenbauer factors.	ggbr_semipara
Goodness-of-Fit test for a garma_model.	gof
Log Likelihood	logLik.garma_model
Plot Forecasts from model.	plot.garma_model
Predict future values.	predict.garma_model
print a garma_model object.	print.garma_model
Print a 'ggbr_factors' object.	print.ggbr_factors
Return the residuals for a GARMA forecast.	residuals.garma_forecast
Residuals	residuals.garma_model
summarise a garma_model object.	summary.garma_model
Diagnostic fit of a garma_model.	tsdiag.garma_model
Covariance matrix	vcov.garma_model