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.

Version: 2.0.2
Depends: R (≥ 4.1.0)
Imports: bayesplot, checkmate, coda (≥ 0.18-1), diagis, dplyr, posterior, Rcpp (≥ 0.12.3), rlang, tidyr
LinkingTo: ramcmc, Rcpp, RcppArmadillo, sitmo
Suggests: covr, ggplot2 (≥ 2.0.0), KFAS (≥ 1.2.1), knitr (≥ 1.11), MASS, rmarkdown (≥ 0.8.1), ramcmc, sde, sitmo, testthat
Published: 2023-10-27
DOI: 10.32614/CRAN.package.bssm
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: pandoc (>= 1.12.3, needed for vignettes)
Citation: bssm citation info
Materials: README 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
Non-linear models with bssm
$\\psi$-APF for non-linear Gaussian state space models
Diffusion models with bssm


Package source: bssm_2.0.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): bssm_2.0.2.tgz, r-oldrel (arm64): bssm_2.0.2.tgz, r-release (x86_64): bssm_2.0.2.tgz, r-oldrel (x86_64): bssm_2.0.2.tgz
Old sources: bssm archive

Reverse dependencies:

Reverse suggests: Ecfun


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