R: Fitting and Forecasting Gegenbauer ARMA Time Series Models

# Fitting and Forecasting Gegenbauer ARMA Time Series Models

## Documentation for package ‘garma’ version 1.0.1

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

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