This package is considered a duplicate. The official version of this package is found at:https://helske.r-universe.dev/bssm

Package: bssm 2.0.2

Jouni Helske

bssm: Bayesian Inference of Non-Linear and Non-Gaussian State Space Models

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.

Authors:Jouni Helske [aut, cre], Matti Vihola [aut]

bssm_2.0.2.tar.gz
bssm_2.0.2.tar.gz(r-4.5-noble)bssm_2.0.2.tar.gz(r-4.4-noble)
bssm_2.0.2.tgz(r-4.4-emscripten)bssm_2.0.2.tgz(r-4.3-emscripten)
bssm.pdf |bssm.html✨
bssm/json (API)
NEWS

# Install 'bssm' in R:

install.packages('bssm', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/helske/bssm/issues

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3
openmp– GCC OpenMP (GOMP) support library

Datasets:

drownings - Deaths by drowning in Finland in 1969-2019
exchange - Pound/Dollar daily exchange rates
negbin_model - Estimated Negative Binomial Model of Helske and Vihola
negbin_series - Simulated Negative Binomial Time Series Data
poisson_series - Simulated Poisson Time Series Data

openblas cpp openmp

3.56 score 11 scripts 1.7k downloads 46 exports 55 dependencies

Last updated 1 years agofrom:7b7b8acf4e. Checks:OK: 2. Indexed: no.

Target	Result	Date
Doc / Vignettes	OK	Dec 02 2024
R-4.5-linux-x86_64	OK	Dec 02 2024

Exports:ar1_lg ar1_ng as_bssm as_draws as_draws_df asymptotic_var bootstrap_filter bsm_lg bsm_ng check_diagnostics cpp_example_model ekf ekf_fast_smoother ekf_smoother ekpf_filter estimate_ess expand_sample fast_smoother gamma gamma_prior gaussian_approx halfnormal halfnormal_prior iact importance_sample kfilter normal normal_prior particle_smoother post_correct run_mcmc sim_smoother smoother ssm_mlg ssm_mng ssm_nlg ssm_sde ssm_ulg ssm_ung suggest_N svm tnormal tnormal_prior ukf uniform uniform_prior

Dependencies:abind backports bayesplot checkmate cli coda colorspace cpp11 diagis distributional dplyr fansi farver generics ggplot2 ggridges glue gridExtra gtable isoband labeling lattice lifecycle magrittr MASS Matrix matrixStats mgcv munsell nlme numDeriv pillar pkgconfig plyr posterior purrr R6 ramcmc RColorBrewer Rcpp RcppArmadillo reshape2 rlang scales sitmo stringi stringr tensorA tibble tidyr tidyselect utf8 vctrs viridisLite withr

$\psi$-APF for non-linear Gaussian state space models

Jouni Helske

Rendered frompsi_pf.Rmdusingknitr::rmarkdownon Dec 02 2024.

Last update: 2023-10-27
Started: 2020-06-09

bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in R

Jouni Helske and Matti Vihola, University of Jyväskylä, Department of Mathematics and Statistics, Finland

Rendered frombssm.Rmdusingknitr::rmarkdownon Dec 02 2024.

Last update: 2023-10-27
Started: 2017-06-25

Diffusion models with bssm

Jouni Helske, University of Jyväskylä, Department of Mathematics and Statistics, Finland

Rendered fromsde_model.Rmdusingknitr::rmarkdownon Dec 02 2024.

Last update: 2023-10-27
Started: 2021-02-22

Non-linear models with bssm

Jouni Helske, University of Jyväskylä, Department of Mathematics and Statistics, Finland

Rendered fromgrowth_model.Rmdusingknitr::rmarkdownon Dec 02 2024.

Last update: 2023-10-27
Started: 2017-06-25

Help page	Topics
Univariate Gaussian model with AR(1) latent process	ar1_lg
Non-Gaussian model with AR(1) latent process	ar1_ng
Convert KFAS Model to bssm Model	as_bssm
Convert 'run_mcmc' Output to 'draws_df' Format	as_draws as_draws.mcmc_output as_draws_df as_draws_df.mcmc_output
Convert MCMC Output to data.frame	as.data.frame.mcmc_output
Asymptotic Variance of IS-type Estimators	asymptotic_var
Bootstrap Filtering	bootstrap_filter bootstrap_filter.lineargaussian bootstrap_filter.nongaussian bootstrap_filter.ssm_nlg bootstrap_filter.ssm_sde
Basic Structural (Time Series) Model	bsm_lg
Non-Gaussian Basic Structural (Time Series) Model	bsm_ng
Bayesian Inference of State Space Models	bssm-package bssm
Quick Diagnostics Checks for 'run_mcmc' Output	check_diagnostics
Example C++ Codes for Non-Linear and SDE Models	cpp_example_model
Deaths by drowning in Finland in 1969-2019	drownings
(Iterated) Extended Kalman Filtering	ekf
Extended Kalman Smoothing	ekf_fast_smoother ekf_smoother
Extended Kalman Particle Filtering	ekpf_filter ekpf_filter.ssm_nlg
Effective Sample Size for IS-type Estimators	estimate_ess
Pound/Dollar daily exchange rates	exchange
Expand the Jump Chain representation	expand_sample
Kalman Smoothing	fast_smoother fast_smoother.lineargaussian smoother smoother.lineargaussian
Fitted for State Space Model	fitted.mcmc_output
Gaussian Approximation of Non-Gaussian/Non-linear State Space Model	gaussian_approx gaussian_approx.nongaussian gaussian_approx.ssm_nlg
Integrated Autocorrelation Time	iact
Importance Sampling from non-Gaussian State Space Model	importance_sample importance_sample.nongaussian
Kalman Filtering	kfilter kfilter.lineargaussian kfilter.nongaussian
Extract Log-likelihood of a State Space Model of class 'bssm_model'	logLik.lineargaussian logLik.nongaussian logLik.ssm_nlg logLik.ssm_sde
Estimated Negative Binomial Model of Helske and Vihola (2021)	negbin_model
Simulated Negative Binomial Time Series Data	negbin_series
Particle Smoothing	particle_smoother particle_smoother.lineargaussian particle_smoother.nongaussian particle_smoother.ssm_nlg particle_smoother.ssm_sde
Trace and Density Plots for 'mcmc_output'	plot.mcmc_output
Simulated Poisson Time Series Data	poisson_series
Run Post-correction for Approximate MCMC using psi-APF	post_correct
Predictions for State Space Models	predict predict.mcmc_output
Print Results from MCMC Run	print.mcmc_output
Bayesian Inference of State Space Models	run_mcmc run_mcmc.lineargaussian run_mcmc.nongaussian run_mcmc.ssm_nlg run_mcmc.ssm_sde
Simulation Smoothing	sim_smoother sim_smoother.lineargaussian sim_smoother.nongaussian
General multivariate linear Gaussian state space models	ssm_mlg
General Non-Gaussian State Space Model	ssm_mng
General multivariate nonlinear Gaussian state space models	ssm_nlg
Univariate state space model with continuous SDE dynamics	ssm_sde
General univariate linear-Gaussian state space models	ssm_ulg
General univariate non-Gaussian state space model	ssm_ung
Suggest Number of Particles for psi-APF Post-correction	suggest_N
Summary Statistics of Posterior Samples	summary.mcmc_output
Stochastic Volatility Model	svm
Unscented Kalman Filtering	ukf
Prior objects for bssm models	bssm_prior bssm_prior_list gamma gamma_prior halfnormal halfnormal_prior normal normal_prior tnormal tnormal_prior uniform uniform_prior

Package: bssm 2.0.2

bssm: Bayesian Inference of Non-Linear and Non-Gaussian State Space Models

$\psi$-APF for non-linear Gaussian state space models

bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in R

Diffusion models with bssm

Non-linear models with bssm

Citation

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)