Efficient methods for Bayesian inference of state space models
via particle Markov chain Monte Carlo and parallel importance sampling type weighted
Markov chain Monte Carlo. Gaussian, Poisson, binomial, or negative binomial
observation densities, stochastic volatility models with Gaussian state
dynamics, as well as general non-linear Gaussian models and discretised diffusion models
are supported.

KFAS (>= 1.2.1), knitr (>= 1.11), rmarkdown (>= 0.8.1),
testthat, bayesplot, sde, ramcmc, sitmo

Bayesian Inference of Non-Gaussian State Space Models
Efficient methods for Bayesian inference of state space models
via particle Markov chain Monte Carlo (MCMC) and MCMC based on parallel
importance sampling type weighted estimators
(Vihola, Helske, and Franks, 2020, <arXiv:1609.02541>).
Gaussian, Poisson, binomial, negative binomial, and Gamma
observation densities and basic stochastic volatility models with Gaussian state
dynamics, as well as general non-linear Gaussian models and discretised
diffusion models are supported.
dplyr, ggplot2 (>= 2.0.0), Hmisc, KFAS (>= 1.2.1), knitr (>=
1.11), MASS, ramcmc, rmarkdown (>= 0.8.1), sde, sitmo, testthat

Efficient methods for Bayesian inference of state space models
via particle Markov chain Monte Carlo (MCMC) and MCMC based on parallel
importance sampling type weighted estimators
(Vihola, Helske, and Franks, 2020, <doi:10.1111/sjos.12492>).
Gaussian, Poisson, binomial, negative binomial, and Gamma
observation densities and basic stochastic volatility models with Gaussian state
dynamics, as well as general non-linear Gaussian models and discretised
diffusion models are supported.

Efficient methods for Bayesian inference of state space models
via particle Markov chain Monte Carlo (MCMC) and MCMC based on parallel
importance sampling type weighted estimators
(Vihola, Helske, and Franks, 2020, <doi:10.1111/sjos.12492>).
Gaussian, Poisson, binomial, negative binomial, and Gamma
observation densities and basic stochastic volatility models
with linear-Gaussian state dynamics,
as well as general non-linear Gaussian models and discretised
diffusion models are supported.