Package: bssm
Type: Package
Title: Bayesian Inference of Non-Linear and Non-Gaussian State Space
        Models
Version: 0.1.4
Date: 2018-02-04
Author: Jouni Helske, Matti Vihola
Maintainer: Jouni Helske <jouni.helske@iki.fi>
Description: Efficient methods for Bayesian inference of state space models 
    via particle Markov chain Monte Carlo and parallel importance sampling type weighted 
    Markov chain Monte Carlo (Vihola, Helske, and Franks, 2017, <arXiv:1609.02541>). 
    Gaussian, Poisson, binomial, or negative binomial 
    observation densities and basic stochastic volatility models with Gaussian state 
    dynamics, as well as general non-linear Gaussian models and discretised diffusion models 
    are supported.
License: GPL (>= 2)
Depends: R (>= 3.1.3)
Suggests: KFAS (>= 1.2.1), knitr (>= 1.11), rmarkdown (>= 0.8.1),
        testthat, bayesplot, sde, ramcmc, sitmo
Imports: coda (>= 0.18-1), diagis, ggplot2 (>= 2.0.0), Rcpp (>= 0.12.3)
LinkingTo: BH, Rcpp, RcppArmadillo, ramcmc, sitmo
SystemRequirements: C++11
RoxygenNote: 6.0.1
VignetteBuilder: knitr
BugReports: https://github.com/helske/bssm/issues
ByteCompile: true
NeedsCompilation: yes
Packaged: 2018-02-04 13:59:28 UTC; jouni
Repository: CRAN
Date/Publication: 2018-02-04 15:30:09 UTC
Built: R 3.3.3; x86_64-w64-mingw32; 2018-04-22 17:42:57 UTC; windows
Archs: i386, x64
