bssm {bssm} | R Documentation |

## Bayesian Inference of State Space Models

### Description

This package contains functions for efficient Bayesian inference of state
space models, where model is assumed to be either

### Details

* Exponential family state space models, where the state equation is linear
Gaussian, and the conditional observation density is either Gaussian,
Poisson, binomial, negative binomial or Gamma density.

* Basic stochastic volatility model.

* General non-linear model with Gaussian noise terms.

* Model with continuous SDE dynamics.

For formal definition of the currently supported models and methods, as
well as some theory behind the IS-MCMC and *psi*-APF,
see Helske and Vihola (2021), Vihola, Helske, Franks (2020) and the package
vignettes.

### References

Helske J, Vihola M (2021). bssm: Bayesian Inference of Non-linear and
Non-Gaussian State Space Models in R. ArXiv 2101.08492,
<URL: https://arxiv.org/abs/2101.08492>.

Vihola, M, Helske, J, Franks, J. (2020). Importance sampling type estimators
based on approximate marginal Markov chain Monte Carlo.
Scand J Statist. 1-38. https://doi.org/10.1111/sjos.12492

[Package

*bssm* version 1.1.7-1

Index]