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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 |
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 |
Package source: | bssm_2.0.2.tar.gz |
Windows binaries: | r-devel: bssm_2.0.2.zip, r-release: bssm_2.0.2.zip, r-oldrel: bssm_2.0.2.zip |
macOS binaries: | r-release (arm64): bssm_2.0.2.tgz, r-oldrel (arm64): bssm_2.0.2.tgz, r-release (x86_64): bssm_2.0.2.tgz, r-oldrel (x86_64): bssm_2.0.2.tgz |
Old sources: | bssm archive |
Reverse suggests: | Ecfun |
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These binaries (installable software) and packages are in development.
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