NGSSEML: Non-Gaussian State Space with Exact Marginal Likelihood

Authors

Thiago R. Santos, Universidade Federal de Minas Gerais
Glaura C. Franco, Universidade Federal de Minas Gerais
Dani Gamerman, Universidade Federal do Rio de Janeiro

Date of this Version

12-2021

Document Type

Article

Citation

The R Journal (December 2021) 13(2); Editor: Dianne Cook

Comments

Copyright 2021, The R Foundation. Open access material. License: CC BY 4.0 International

Abstract

The number of packages/software for Gaussian State Space models has increased over recent decades. However, there are very few codes available for non-Gaussian State Space (NGSS) models due to analytical intractability that prevents exact calculations. One of the few tractable exceptions is the family of NGSS with exact marginal likelihood, named NGSSEML. In this work, we present the wide range of data formats and distributions handled by NGSSEML and a package in the R language to perform classical and Bayesian inference for them. Special functions for filtering, forecasting, and smoothing procedures and the exact calculation of the marginal likelihood function are provided. The methods implemented in the package are illustrated for count and volatility time series and some reliability/survival models, showing that the codes are easy to handle. Therefore, the NGSSEML family emerges as a simple and interesting option/alternative for modeling non-Gaussian time-varying structures commonly encountered in time series and reliability/survival studies.