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. Gaussian, Poisson, binomial, or negative binomial observation densities, stochastic volatility models with Gaussian state dynamics, as well as general non-linear Gaussian models and discretised diffusion models are supported.

Version: 0.1.2
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: 2017-11-22
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: bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in R
Logistic growth model with bssm
Package source: bssm_0.1.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X El Capitan binaries: r-release: bssm_0.1.2.tgz
OS X Mavericks binaries: r-oldrel: bssm_0.1.1-1.tgz
Old sources: bssm archive


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