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.">

bssm: Bayesian Inference of Non-Linear and Non-Gaussian State Space Models (original) (raw)

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 iki.fi>
BugReports: https://github.com/helske/bssm/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/helske/bssm
NeedsCompilation: yes
SystemRequirements: pandoc (>= 1.12.3, needed for vignettes)
Citation: bssm citation info
Materials: README NEWS
In views: TimeSeries
CRAN checks: bssm results

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