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bayesSSM: Bayesian Methods for State Space Models

Implements methods for Bayesian analysis of State Space Models. Includes implementations the Particle Marginal Metropolis-Hastings algorithm described in Andrieu et al. (2010) <doi:10.1111/j.1467-9868.2009.00736.x> and automatic tuning inspired by Pitt et al. (2012) <doi:10.1016/j.jeconom.2012.06.004> and J. Dahlin and T. B. Schön (2019) <doi:10.18637/jss.v088.c02>.

Version: 0.4.7
Imports: MASS, stats, dplyr, future, future.apply
Suggests: knitr, rmarkdown, testthat (≥ 3.0.0), ggplot2, tidyr, extraDistr
Published: 2025-04-23
DOI: 10.32614/CRAN.package.bayesSSM
Author: Bjarke Hautop [aut, cre, cph]
Maintainer: Bjarke Hautop <bjarke.hautop at gmail.com>
BugReports: https://github.com/BjarkeHautop/bayesSSM/issues
License: MIT + file LICENSE
URL: https://github.com/BjarkeHautop/bayesSSM, https://bjarkehautop.github.io/bayesSSM/
NeedsCompilation: no
Materials: README NEWS
CRAN checks: bayesSSM results

Documentation:

Reference manual: bayesSSM.pdf
Vignettes: bayesSSM (source, R code)

Downloads:

Package source: bayesSSM_0.4.7.tar.gz
Windows binaries: r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries: r-release (arm64): bayesSSM_0.4.7.tgz, r-oldrel (arm64): bayesSSM_0.4.7.tgz, r-release (x86_64): bayesSSM_0.4.7.tgz, r-oldrel (x86_64): bayesSSM_0.4.7.tgz

Linking:

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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.