Formulating State Space Models in R with Focus on Longitudinal Regression Models

Abstract

We provide a language for formulating a range of state space models with response densities within the exponential family. The described methodology is implemented in the R-package sspir. A state space model is specified similarly to a generalized linear model in R, and then the time-varying terms are marked in the formula. Special functions for specifying polynomial time trends, harmonic seasonal patterns, unstructured seasonal patterns and time-varying covariates can be used in the formula. The model is fitted to data using iterated extended Kalman filtering, but the formulation of models does not depend on the implemented method of inference. The package is demonstrated on three datasets.