Package: bssm Type: Package Title: Bayesian Inference of Non-Linear and Non-Gaussian State Space Models Version: 2.0.2 Authors@R: c(person(given = "Jouni", family = "Helske", role = c("aut", "cre"), email = "jouni.helske@iki.fi", comment = c(ORCID = "0000-0001-7130-793X")), person(given = "Matti", family = "Vihola", role = "aut", comment = c(ORCID = "0000-0002-8041-7222"))) Description: 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, ), 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, ) for details. License: GPL (>= 2) Depends: R (>= 4.1.0) Suggests: covr, ggplot2 (>= 2.0.0), KFAS (>= 1.2.1), knitr (>= 1.11), MASS, rmarkdown (>= 0.8.1), ramcmc, sde, sitmo, testthat Imports: bayesplot, checkmate, coda (>= 0.18-1), diagis, dplyr, posterior, Rcpp (>= 0.12.3), rlang, tidyr LinkingTo: ramcmc, Rcpp, RcppArmadillo, sitmo SystemRequirements: pandoc (>= 1.12.3, needed for vignettes) VignetteBuilder: knitr BugReports: https://github.com/helske/bssm/issues URL: https://github.com/helske/bssm ByteCompile: true Encoding: UTF-8 NeedsCompilation: yes RoxygenNote: 7.2.3 Packaged: 2023-10-27 10:43:10 UTC; jvhels Author: Jouni Helske [aut, cre] (), Matti Vihola [aut] () Maintainer: Jouni Helske Repository: CRAN Date/Publication: 2023-10-27 12:00:03 UTC