NGSSEML: Non-Gaussian State-Space with Exact Marginal Likelihood

Due to a large quantity of non-Gaussian time series and reliability data, the R-package non-Gaussian state-space with exact marginal likelihood is useful for modeling and forecasting non-Gaussian time series and reliability data via non-Gaussian state-space models with the exact marginal likelihood easily, see Gamerman, Santos and Franco (2013) <doi:10.1111/jtsa.12039> and Santos, Gamerman and Franco (2017) <doi:10.1109/TR.2017.2670142>. The package gives codes for formulating and specifying the non-Gaussian state-space models in the R language. Inferences for the parameters of the model can be made under the classical and Bayesian. Furthermore, prediction, filtering, and smoothing procedures can be used to perform inferences for the latent parameters. Applications include, e.g., count, volatility, piecewise exponential, and software reliability data.

Getting started

Package details

AuthorThiago Rezende dos Santos <>, Dani Gamerman <>, Glaura da Conceicao Franco <>
MaintainerT. R. dos Santos <>
LicenseGPL (>= 2)
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:

Try the NGSSEML package in your browser

Any scripts or data that you put into this service are public.

NGSSEML documentation built on Sept. 5, 2021, 5:36 p.m.