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garma - R package for estimation of Gegenbauer Seasonal/Cyclical long memory processes.

Overview & Introduction

This package fits a GARMA model (refer documentation) to a univariate time series.

GARMA models are extensions of ARIMA models which allow for both fractional differencing (like "fracdiff") but also allow that to happen at a non-zero frequency in the spectrum.

This package will estimate that frequency (which is known for technical reasons as the "Gegenbauer" frequency).

At time of writing several estimation methods are supports as well as a number of (non-linear) optimisation routines.

Installation.

The package can be installed from CRAN in the usual manner:

> install.packages('garma')

Documentation

An Introduction to the "garma" packages is available here, and the reference documentation is available here.

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Install

install.packages('garma')

Monthly Downloads

526

Version

0.9.6

License

GPL-3

Issues

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Maintainer

Richard Hunt

Last Published

October 29th, 2020

Functions in garma (0.9.6)

garma_ggtsdisplay

ggtsdisplay of underlying ARMA process.
extract_arma

Extract underlying ARMA process.
ggbr_semipara

Extract semiparametric estimates of the Gegenbauer factors.
gg_raw_pgram

Display raw periodogram
forecast.garma_model

Forecast future values.
ggplot.garma_model

ggplot of the Forecasts of the model.
vcov.garma_model

Covariance matrix
version

garma package version
print.garma_model

print a garma_model object.
predict.garma_model

Predict future values.
residuals.garma_model

Residuals
print.garma_semipara

Print Semiparametric Estimates
summary.garma_model

summarise a garma_model object.
print.ggbr_factors

Print a 'ggbr_factors' object.
logLik.garma_model

Log Likelihood
plot.garma_model

Plot Forecasts from model.
coef.garma_model

Model Coefficients
garma

garma: A package for estimating and foreasting Gegenbauer time series models.
fitted.garma_model

Fitted values
AIC.garma_model

AIC for model