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.


Reference manual

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0.1.2 by Jouni Helske, 3 days ago

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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