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 |

asymptotic_var | Asymptotic Variance of IS-type Estimators |

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 |

gamma | 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 |

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.gaussian | Kalman Filtering |

kfilter.nongaussian | Kalman Filtering |

logLik.gaussian | Log-likelihood of a Gaussian State Space Model |

logLik.nongaussian | Log-likelihood of a Non-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.gaussian | 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 |

post_correct | Run Post-correction for Approximate MCMC using psi-APF |

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 |

suggest_N | Suggest Number of Particles for psi-APF Post-correction |

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 |