bssm: an R package for Bayesian inference of state space models
Efficient methods for Bayesian inference of state space models via particle Markov chain Monte Carlo and importance sampling type weighted Markov chain Monte Carlo. Currently Gaussian, Poisson, binomial, or negative binomial observation densities and linear-Gaussian state dynamics, as well as general non-linear Gaussian models are supported.
For details, see package vignette and paper Importance sampling type correction of Markov chain Monte Carlo and exact approximations. There is also a separate vignette for non-linear Gaussian models, and couple posters related to IS-correction methodology:
Now on CRAN. Still under development, pull requests very welcome especially related to post-processing, visualization, and C++ modularization.
You can install the latest development version by using the devtools package: