Functions are provided for estimation, testing, diagnostic checking and forecasting of generalized linear autoregressive moving average (GLARMA) models for discrete valued time series with regression variables. These are a class of observation driven non-linear non-Gaussian state space models. The state vector consists of a linear regression component plus an observation driven component consisting of an autoregressive-moving average (ARMA) filter of past predictive residuals. Currently three distributions (Poisson, negative binomial and binomial) can be used for the response series. Three options (Pearson, score-type and unscaled) for the residuals in the observation driven component are available. Estimation is via maximum likelihood (conditional on initializing values for the ARMA process) optimized using Fisher scoring or Newton Raphson iterative methods. Likelihood ratio and Wald tests for the observation driven component allow testing for serial dependence in generalized linear model settings. Graphical diagnostics including model fits, autocorrelation functions and probability integral transform residuals are included in the package. Several standard data sets are included in the package.

Documentation

Manual: glarma.pdf
Vignette: The glarma package

Maintainer: "William T.M. Dunsmuir" <w.dunsmuir at unsw.edu.au>

Author(s): William T.M. Dunsmuir <w.dunsmuir at unsw.edu.au>, Cenanning Li <cli113 at aucklanduni.ac.nz>, and David J. Scott <d.scott at auckland.ac.nz>

Install package and any missing dependencies by running this line in your R console:

install.packages("glarma")

Depends R (>= 2.3.0)
Imports MASS
Suggests RUnit, knitr, zoo
Enhances
Linking to
Reverse
depends
Reverse
imports
Reverse
suggests
Reverse
enhances
Reverse
linking to

Package glarma
Materials
URL
Task Views TimeSeries
Version 1.5-0
Published 2017-01-25
License GPL (>= 2)
BugReports
SystemRequirements
NeedsCompilation no
Citation
CRAN checks glarma check results
Package source glarma_1.5-0.tar.gz