DESCRIPTION

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, <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.
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
Roxygen: list(markdown = TRUE, 
  roclets = c("namespace", "rd", "srr::srr_stats_roclet"))