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 nonlinear nonGaussian state space models. The state vector consists of a linear regression component plus an observation driven component consisting of an autoregressivemoving 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, scoretype 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.
Package details 


Author  William T.M. Dunsmuir <[email protected]>, Cenanning Li <[email protected]>, and David J. Scott <[email protected]> 
Maintainer  "William T.M. Dunsmuir" <[email protected]> 
License  GPL (>= 2) 
Version  1.60 
Package repository  View on CRAN 
Installation 
Install the latest version of this package by entering the following in R:

Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.