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 [aut, cre], Matti Vihola [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 Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=bssm to link to this page.
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