bssm: Bayesian Inference of Non-Gaussian State Space Models

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, <doi:10.1111/sjos.12492>). 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: 1.0.1-1
Depends: R (≥ 3.5.0)
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: 2020-11-12
Author: Jouni Helske ORCID iD [aut, cre], Matti Vihola ORCID iD [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

Downloads:

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_1.0.1-1.tar.gz
Windows binaries: r-devel: bssm_1.0.1-1.zip, r-release: bssm_1.0.1-1.zip, r-oldrel: bssm_1.0.1-1.zip
macOS binaries: r-release: bssm_1.0.1-1.tgz, r-oldrel: bssm_1.0.1-1.tgz
Old sources: bssm archive

Reverse dependencies:

Reverse suggests: Ecfun

Linking:

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