Bayesian Inference of Non-Linear and Non-Gaussian State Space Models R package bssm version 2.0.3 CRAN Task View: Bayesian (https://CRAN.R-project.org/view=Bayesian) CRAN Task View: TimeSeries (https://CRAN.R-project.org/view=TimeSeries) 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. Software 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 Jouni Helske <jouni.helske@iki.fi> Comprehensive R Archive Network (CRAN) Jouni Helske [aut, cre] (ORCID: <https://orcid.org/0000-0001-7130-793X>), Matti Vihola [aut] (ORCID: <https://orcid.org/0000-0002-8041-7222>) GPL (>= 2) 2025-09-24 application/tgz https://CRAN.R-project.org/package=bssm doi:10.32614/CRAN.package.bssm