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 1969-2019 |
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 | 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.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 |