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bssm
View on CRAN: Click
here
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]
[Scholar Profile | Author Map]
All associated links for this package
10.32614/CRAN.package.bssm . https://github.com/helske/bssm/issues . https://github.com/helske/bssm . bssm citation info . bssm results . bssm.pdf . bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in R . Non-linear models with bssm . Diffusion models with bssm . bssm_2.0.2.tar.gz . bssm_2.0.2.zip . bssm_2.0.2.zip . bssm_2.0.2.zip . bssm_2.0.2.tgz . bssm_2.0.2.tgz . bssm_2.0.2.tgz . bssm_2.0.2.tgz . bssm_2.0.2.tgz . bssm_2.0.2.tgz . bssm archive . https://CRAN.R-project.org/package=bssm .
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. Accessed 03 Apr. 2025.
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)
Other packages that cited bssm R package
View bssm citation profile
Other R packages that bssm depends,
imports, suggests or enhances
Complete documentation for bssm
Functions, R codes and Examples using
the bssm R package
Some associated functions: ar1_lg . ar1_ng . as.data.frame.mcmc_output . as_bssm . as_draws-mcmc_output . asymptotic_var . bootstrap_filter . bsm_lg . bsm_ng . bssm . bssm_prior . check_diagnostics . cpp_example_model . drownings . ekf . ekf_smoother . ekpf_filter . estimate_ess . exchange . expand_sample . fitted.mcmc_output . gaussian_approx . iact . importance_sample . kfilter . logLik_bssm . negbin_model . negbin_series . particle_smoother . poisson_series . post_correct . predict.mcmc_output . print.mcmc_output . run_mcmc . sim_smoother . smoother . ssm_mlg . ssm_mng . ssm_nlg . ssm_sde . ssm_ulg . ssm_ung . suggest_N . summary.mcmc_output . svm . ukf .
Some associated R codes: RcppExports.R . approx.R . as.data.frame.mcmc_output.R . as_bssm.R . as_draws.R . asymptotic_var.R . bootstrap_filter.R . bssm-package.R . check_arguments.R . check_diagnostics.R . cpp_example_models.R . ekpf_filter.R . expand_sample.R . fitted.R . importance_sample.R . init_mode.R . kfilter.R . loglik.R . model_type.R . models.R . particle_smoother.R . post_correction.R . predict.R . print_mcmc.R . priors.R . run_mcmc.R . sim_smoother.R . smoother.R . srr-stats-standards.R . summary.R . zzz.R . Full bssm package functions and examples
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