Efficient methods for Bayesian inference of state space models via Markov chain Monte Carlo (MCMC) based on parallel importance sampling type weighted estimators (Vihola, Helske, and Franks, 2020, <doi:10.1111/sjos.12492>), particle MCMC, and its delayed acceptance version. Gaussian, Poisson, binomial, negative binomial, and Gamma observation densities and basic stochastic volatility models with linearGaussian state dynamics, as well as general nonlinear Gaussian models and discretised diffusion models are supported. See Helske and Vihola (2021, <doi:10.32614/RJ2021103>) for details.
Package details 


Author  Jouni Helske [aut, cre] (<https://orcid.org/000000017130793X>), Matti Vihola [aut] (<https://orcid.org/0000000280417222>) 
Maintainer  Jouni Helske <jouni.helske@iki.fi> 
License  GPL (>= 2) 
Version  2.0.1 
URL  https://github.com/helske/bssm 
Package repository  View on CRAN 
Installation 
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