Bayesian Inference of Non-Linear and Non-Gaussian State Space Models

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.


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install.packages("bssm")

0.1.2 by Jouni Helske, 3 days ago


Report a bug at https://github.com/helske/bssm/issues


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


Authors: Jouni Helske, Matti Vihola


Documentation:   PDF Manual  


GPL (>= 2) license


Imports coda, diagis, ggplot2, Rcpp

Suggests KFAS, knitr, rmarkdown, testthat, bayesplot

Linking to BH, Rcpp, RcppArmadillo, ramcmc, sitmo

System requirements: C++11


See at CRAN