NLNG: A R Package for State Space Models

Abstract

State space models require the ability to perform filtering, smoothing and prediction during analysis. To perform these procedures fairly complex computational algorithms are required. There is a consequent need for software tools to facilitate the implementation of state space models. One of leading choices for computation data analysis is the statistical programming language, R. And a key area where analysis tools are lacking is for advanced state space models. This thesis outlines the development of a comprehensive toolkit for nonlinear and non-Gaussian state space models: the nLnG package.