bssm: 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, <arXiv:1609.02541>). 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.

bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in R Logistic growth model with bssm bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in R" Logistic growth model with bssm"

Vignettes Man pages API and functions Files

Package details
Author	Jouni Helske, Matti Vihola
Maintainer	Jouni Helske <[email protected]>
License	GPL (>= 2)
Version	0.1.6-1
Package repository	View on CRAN
Installation	Install the latest version of this package by entering the following in R: `install.packages("bssm")`