bssm (0.1.8)

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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 (Vihola, Helske, and Franks, 2017, ). Gaussian, Poisson, binomial, or negative binomial 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.

Maintainer: Jouni Helske
Author(s): Jouni Helske [aut, cre] (<>), Matti Vihola [aut] (<>)

License: GPL (>= 2)

Uses: coda, diagis, ggplot2, Rcpp, sde, KFAS, MASS, testthat, knitr, rmarkdown, sitmo, bayesplot, ramcmc

Released 2 months ago.

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