bssm: Bayesian Inference of State Space Models

Efficient methods for Bayesian inference of state space models via particle Markov chain Monte Carlo and importance sampling type corrected Markov chain Monte Carlo. Gaussian, Poisson, binomial, or negative binomial observation densities and Gaussian state dynamics, as well as general non-linear Gaussian models are supported.

Version: 0.1.1-1
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: KFAS (≥ 1.2.1), knitr (≥ 1.11), rmarkdown (≥ 0.8.1), testthat, bayesplot
Published: 2017-07-12
Author: Jouni Helske, Matti Vihola
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
CRAN checks: bssm results


Reference manual: bssm.pdf
Vignettes: Bayesian Inference of State Space Models
Logistic growth model with bssm
Package source: bssm_0.1.1-1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X El Capitan binaries: r-release: bssm_0.1.1-1.tgz
OS X Mavericks binaries: r-oldrel: bssm_0.1.1-1.tgz
Old sources: bssm archive


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