Package: bssm
Type: Package
Title: Bayesian Inference of Non-Linear and Non-Gaussian State Space
        Models
Version: 0.1.2
Date: 2017-11-21
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. Gaussian, Poisson, binomial, or negative binomial 
    observation densities, 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
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: 2017-11-22 08:43:43 UTC; jouhe21
Repository: CRAN
Date/Publication: 2017-11-22 17:03:32 UTC
