bssm: Bayesian Inference of Non-Linear and Non-Gaussian State Space
Models
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.
| Version: |
2.0.2 |
| Depends: |
R (≥ 4.1.0) |
| Imports: |
bayesplot, 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: |
2023-10-27 |
| DOI: |
10.32614/CRAN.package.bssm |
| 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: |
pandoc (>= 1.12.3, needed for vignettes) |
| Citation: |
bssm citation info |
| Materials: |
README NEWS |
| In views: |
TimeSeries |
| CRAN checks: |
bssm results |
Documentation:
Downloads:
Reverse dependencies:
Linking:
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