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) and Santos, Gamerman and Franco (2017) . 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.


News

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("NGSSEML")

2.1 by T. R. dos Santos, 7 months ago


https://github.com/hadht/NGSSEML-R-Package


Browse source code at https://github.com/cran/NGSSEML


Authors: Thiago Rezende dos Santos <[email protected]> , Dani Gamerman <[email protected]> , Glaura da Conceicao Franco <[email protected]>


Documentation:   PDF Manual  


Task views: Time Series Analysis, Bayesian Inference


GPL (>= 2) license


Imports mvtnorm, fields, dlm, car, interp

Depends on R.1, R.2, R.3, R.4, R.5, R.6


See at CRAN