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.
| Version: | 0.1.2 |
| Depends: | R (≥ 3.1.3) |
| Imports: | coda (≥ 0.18-1), diagis, ggplot2 (≥ 2.0.0), Rcpp (≥ 0.12.3) |
| LinkingTo: | BH, Rcpp, RcppArmadillo, ramcmc, sitmo |
| Suggests: | KFAS (≥ 1.2.1), knitr (≥ 1.11), rmarkdown (≥ 0.8.1), testthat, bayesplot |
| Published: | 2017-11-22 |
| Author: | Jouni Helske, Matti Vihola |
| Maintainer: | Jouni Helske <jouni.helske at iki.fi> |
| BugReports: | https://github.com/helske/bssm/issues |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | yes |
| SystemRequirements: | C++11 |
| Citation: | bssm citation info |
| Materials: | NEWS |
| CRAN checks: | bssm results |
| Reference manual: | bssm.pdf |
| Vignettes: |
bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in R Logistic growth model with bssm |
| Package source: | bssm_0.1.2.tar.gz |
| Windows binaries: | r-devel: bssm_0.1.2.zip, r-release: bssm_0.1.2.zip, r-oldrel: bssm_0.1.1-1.zip |
| OS X El Capitan binaries: | r-release: bssm_0.1.2.tgz |
| OS X Mavericks binaries: | r-oldrel: bssm_0.1.1-1.tgz |
| Old sources: | bssm archive |
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