The R Journal: article published in 2021, volume 13:2

NGSSEML: Non-Gaussian State Space with Exact Marginal Likelihood
Thiago R. Santos, Glaura C. Franco and Dani Gamerman , The R Journal (2021) 13:2, pages 208-227.

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. Keywords: Bayesian, classical inference, reliability, smoothing, time series, software R

Received: 2020-06-25; online 2021-10-25, supplementary material, (3 KiB)
CRAN packages: StructTS, dlm, dlmodeler, SSsimple, MARSS, sspir, pomp, KFAS, bssm, dynamichazard, NGSSEML, coda
CRAN Task Views implied by cited CRAN packages: TimeSeries, Bayesian, DifferentialEquations, Finance, GraphicalModels

This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2021-087,
  author = {Thiago R. Santos and Glaura C. Franco and Dani Gamerman},
  title = {{NGSSEML: Non-Gaussian State Space with Exact Marginal
          Likelihood}},
  year = {2021},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2021-087},
  url = {https://doi.org/10.32614/RJ-2021-087},
  pages = {208--227},
  volume = {13},
  number = {2}
}