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.

Version: 0.1.11
Depends: R (≥ 3.1.3)
Imports: coda (≥ 0.18-1), diagis, ggplot2 (≥ 2.0.0), Rcpp (≥ 0.12.3)
LinkingTo: BH, Rcpp, RcppArmadillo, ramcmc, sitmo
Suggests: bayesplot, KFAS (≥ 1.2.1), knitr (≥ 1.11), MASS, ramcmc, rmarkdown (≥ 0.8.1), sde, sitmo, testthat
Published: 2020-02-26
Author: Jouni Helske ORCID iD [aut, cre], Matti Vihola ORCID iD [aut]
Maintainer: Jouni Helske <jouni.helske at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
SystemRequirements: C++11
Citation: bssm citation info
Materials: NEWS
In views: TimeSeries
CRAN checks: bssm results


Reference manual: bssm.pdf
Vignettes: bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in R
Logistic growth model with bssm
Package source: bssm_0.1.11.tar.gz
Windows binaries: r-devel:, r-devel-gcc8:, r-release:, r-oldrel:
OS X binaries: r-release: bssm_0.1.11.tgz, r-oldrel: bssm_0.1.11.tgz
Old sources: bssm archive

Reverse dependencies:

Reverse suggests: Ecfun


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