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.
| Version: |
2.0.0 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
magrittr, checkmate, coda (≥ 0.18-1), diagis, dplyr, posterior, Rcpp (≥ 0.12.3), rlang, tidyr |
| LinkingTo: |
ramcmc, Rcpp, RcppArmadillo, sitmo |
| Suggests: |
covr, ggplot2 (≥ 2.0.0), KFAS (≥ 1.2.1), knitr (≥ 1.11), MASS, rmarkdown (≥ 0.8.1), ramcmc, sde, sitmo, testthat |
| Published: |
2021-11-26 |
| Author: |
Jouni Helske 
[aut, cre],
Matti Vihola
[aut] |
| Maintainer: |
Jouni Helske <jouni.helske at iki.fi> |
| BugReports: |
https://github.com/helske/bssm/issues |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: |
https://github.com/helske/bssm |
| NeedsCompilation: |
yes |
| SystemRequirements: |
C++11, pandoc (>= 1.12.3, needed for vignettes) |
| Citation: |
bssm citation info |
| Materials: |
README NEWS |
| In views: |
TimeSeries |
| CRAN checks: |
bssm results |