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

# Bayesian Inference of Non-Linear and Non-Gaussian State Space Models

## Documentation for package ‘bssm’ version 2.0.3

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

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