# Bayesian Inference of Non-Gaussian State Space Models

## Documentation for package ‘bssm’ version 1.0.0

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

## Help Pages

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 chain to data.frame
as_bssm | Convert KFAS Model to bssm Model
bootstrap_filter | Bootstrap Filtering
bootstrap_filter.gaussian | 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
drownings | Deaths by drowning in Finland in 1969-2014
ekf | (Iterated) Extended Kalman Filtering
ekf_smoother | Extended Kalman Smoothing
ekpf_filter | Extended Kalman Particle Filtering
ekpf_filter.ssm_nlg | Extended Kalman Particle Filtering
exchange | Pound/Dollar daily exchange rates
expand_sample | Expand the Jump Chain representation
fast_smoother | Kalman Smoothing
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
importance_sample | Importance Sampling from non-Gaussian State Space Model
importance_sample.nongaussian | Importance Sampling from non-Gaussian State Space Model
kfilter | Kalman Filtering
logLik.gaussian | Log-likelihood of a Gaussian State Space Model
logLik.nongaussian | Log-likelihood of a Gaussian State Space Model
logLik.ssm_nlg | Log-likelihood of a Non-linear State Space Model
logLik.ssm_sde | Log-likelihood of a State Space Model with SDE dynamics
normal | Prior objects for bssm models
particle_smoother | Particle Smoothing
particle_smoother.nongaussian | Particle Smoothing
particle_smoother.ssm_nlg | Particle Smoothing
particle_smoother.ssm_sde | Particle Smoothing
poisson_series | Simulated Poisson time series data
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.gaussian | Bayesian Inference of Linear-Gaussian State Space Models
run_mcmc.nongaussian | Bayesian Inference of Non-Gaussian State Space Models
run_mcmc.ssm_nlg | Bayesian Inference of non-linear state space models
run_mcmc.ssm_sde | Bayesian Inference of SDE
sim_smoother | Simulation Smoothing
sim_smoother.gaussian | Simulation Smoothing
sim_smoother.nongaussian | Simulation Smoothing
smoother | 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
summary.mcmc_output | Summary of MCMC object
svm | Stochastic Volatility Model
tnormal | Prior objects for bssm models
ukf | Unscented Kalman Filtering
uniform | Prior objects for bssm models