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bssm  

Bayesian Inference of Non-Linear and Non-Gaussian State Space Models
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Download and install bssm package within the R console
Install from CRAN:
install.packages("bssm")

Install from Github:
library("remotes")
install_github("cran/bssm")

Install by package version:
library("remotes")
install_version("bssm", "2.0.2")



Attach the package and use:
library("bssm")
Maintained by
Jouni Helske
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2017-06-25
Latest Update: 2023-10-27
Description:
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, ), 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, ) for details.
How to cite:
Jouni Helske (2017). bssm: Bayesian Inference of Non-Linear and Non-Gaussian State Space Models. R package version 2.0.2, https://cran.r-project.org/web/packages/bssm
Previous versions and publish date:
0.1.0 (2017-06-25 09:14), 0.1.1-1 (2017-07-12 16:39), 0.1.1 (2017-06-27 23:16), 0.1.2 (2017-11-22 18:03), 0.1.3 (2018-01-25 14:07), 0.1.4 (2018-02-04 16:30), 0.1.5 (2018-05-23 16:54), 0.1.6-1 (2018-11-22 14:50), 0.1.7 (2019-04-09 23:36), 0.1.8-1 (2020-01-07 09:50), 0.1.8 (2019-09-25 15:50), 0.1.9 (2020-01-28 10:10), 0.1.10 (2020-02-04 09:40), 0.1.11 (2020-02-26 16:40), 1.0.0 (2020-06-09 17:20), 1.0.1-1 (2020-11-12 22:40), 1.1.0 (2021-01-20 12:20), 1.1.2 (2021-02-08 17:00), 1.1.3-1 (2021-02-22 15:10), 1.1.3-2 (2021-02-26 22:40), 1.1.4 (2021-04-13 18:30), 1.1.5 (2021-07-10 10:50), 1.1.6 (2021-09-06 08:40), 1.1.7-1 (2021-09-21 15:10), 1.1.7 (2021-09-20 08:50), 2.0.0 (2021-11-26 16:00), 2.0.1 (2022-05-04 01:00)
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