Package: bssm
Type: Package
Title: Bayesian Inference of Non-Linear and Non-Gaussian State Space
        Models
Version: 2.0.0
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 particle Markov chain Monte Carlo (MCMC) and MCMC based on parallel 
    importance sampling type weighted estimators 
    (Vihola, Helske, and Franks, 2020, <doi:10.1111/sjos.12492>). 
    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.
License: GPL (>= 2)
Depends: R (>= 3.5.0)
Suggests: covr, ggplot2 (>= 2.0.0), KFAS (>= 1.2.1), knitr (>= 1.11),
        MASS, rmarkdown (>= 0.8.1), ramcmc, sde, sitmo, testthat
Imports: magrittr, checkmate, coda (>= 0.18-1), diagis, dplyr,
        posterior, Rcpp (>= 0.12.3), rlang, tidyr
LinkingTo: ramcmc, Rcpp, RcppArmadillo, sitmo
SystemRequirements: C++11, 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: 7.1.2
Packaged: 2021-11-25 22:06:48 UTC; jovetale
Author: Jouni Helske [aut, cre] (<https://orcid.org/0000-0001-7130-793X>),
  Matti Vihola [aut] (<https://orcid.org/0000-0002-8041-7222>)
Maintainer: Jouni Helske <jouni.helske@iki.fi>
Repository: CRAN
Date/Publication: 2021-11-26 15:00:02 UTC
Built: R 4.0.2; x86_64-apple-darwin17.0; 2021-11-27 12:41:01 UTC; unix
Archs: bssm.so.dSYM
