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

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 linear-Gaussian state dynamics, as well as general non-linear Gaussian models and discretised diffusion models are supported. See Helske and Vihola (2021, <doi:10.32614/RJ-2021-103>) for details.

README.md bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in R Diffusion models with bssm Non-linear models with bssm $\\psi$-APF for non-linear Gaussian state space models

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Package details
Author	Jouni Helske [aut, cre] (<https://orcid.org/0000-0001-7130-793X>), Matti Vihola [aut] (<https://orcid.org/0000-0002-8041-7222>)
Maintainer	Jouni Helske <jouni.helske@iki.fi>
License	GPL (>= 2)
Version	2.0.2
URL	https://github.com/helske/bssm
Package repository	View on CRAN
Installation	Install the latest version of this package by entering the following in R: `install.packages("bssm")`