[aut, cre],
Matti Vihola
[aut]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, <arXiv:1609.02541>). Gaussian, Poisson, binomial, negative binomial, and Gamma 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: | 1.0.0 |
| Depends: | R (≥ 3.1.3) |
| Imports: | coda (≥ 0.18-1), diagis, Rcpp (≥ 0.12.3) |
| LinkingTo: | Rcpp, RcppArmadillo, ramcmc, sitmo |
| Suggests: | dplyr, ggplot2 (≥ 2.0.0), Hmisc, KFAS (≥ 1.2.1), knitr (≥ 1.11), MASS, ramcmc, rmarkdown (≥ 0.8.1), sde, sitmo, testthat |
| Published: | 2020-06-09 |
| 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 Non-linear modelsl with bssm $\\psi$-APF for non-linear Gaussian state space models |
| Package source: | bssm_1.0.0.tar.gz |
| Windows binaries: | r-devel: bssm_1.0.0.zip, r-release: bssm_1.0.0.zip, r-oldrel: bssm_1.0.0.zip |
| macOS binaries: | r-release: bssm_1.0.0.tgz, r-oldrel: bssm_1.0.0.tgz |
| Old sources: | bssm archive |
| Reverse suggests: | Ecfun |
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