[aut, cre],
Matti Vihola
[aut]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.
| Version: | 0.1.11 |
| 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: | bayesplot, KFAS (≥ 1.2.1), knitr (≥ 1.11), MASS, ramcmc, rmarkdown (≥ 0.8.1), sde, sitmo, testthat |
| Published: | 2020-02-26 |
| 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)] |
| NeedsCompilation: | yes |
| SystemRequirements: | C++11 |
| Citation: | bssm citation info |
| Materials: | NEWS |
| In views: | TimeSeries |
| 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_0.1.11.tar.gz |
| Windows binaries: | r-devel: bssm_0.1.11.zip, r-devel-gcc8: bssm_0.1.11.zip, r-release: bssm_0.1.11.zip, r-oldrel: bssm_0.1.11.zip |
| OS X binaries: | r-release: bssm_0.1.11.tgz, r-oldrel: bssm_0.1.11.tgz |
| Old sources: | bssm archive |
| Reverse suggests: | Ecfun |
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