bssm-package | Bayesian Inference of State Space Models |
ar1 | Univariate Gaussian model with AR(1) latent process |
as_gssm | Convert SSModel Object to gssm or ngssm Object |
as_ngssm | Convert SSModel Object to gssm or ngssm Object |
autoplot.predict_bssm | Plot predictions based on bssm package |
bootstrap_filter | Bootstrap Filtering |
bootstrap_filter.ngssm | Bootstrap Filtering |
bootstrap_filter.nlg_ssm | Bootstrap Filtering |
bootstrap_filter.sde_ssm | Bootstrap Filtering |
bootstrap_filter.svm | Bootstrap Filtering |
bsm | 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.nlg_ssm | 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 state space model |
gaussian_approx.ng_bsm | Gaussian approximation of non-Gaussian state space model |
gssm | General univariate linear-Gaussian state space models |
halfnormal | Prior objects for bssm models |
importance_sample | Importance Sampling from non-Gaussian State Space Model |
importance_sample.ngssm | Importance Sampling from non-Gaussian State Space Model |
importance_sample.ng_bsm | Importance Sampling from non-Gaussian State Space Model |
importance_sample.svm | Importance Sampling from non-Gaussian State Space Model |
importance_sample.ung_ar1 | Importance Sampling from non-Gaussian State Space Model |
kfilter | Kalman Filtering |
lgg_ssm | General multivariate linear Gaussian state space models |
logLik.gssm | Log-likelihood of the State Space Model |
logLik.ngssm | Log-likelihood of the State Space Model |
mv_gssm | General multivariate linear-Gaussian state space models |
ngssm | General univariate non-Gaussian/non-linear state space models |
ng_ar1 | Non-Gaussian model with AR(1) latent process |
ng_bsm | Non-Gaussian Basic Structural (Time Series) Model |
nlg_ssm | General multivariate nonlinear Gaussian state space models |
normal | Prior objects for bssm models |
particle_smoother | Particle Smoothing |
particle_smoother.gssm | Particle Smoothing |
particle_smoother.ngssm | Particle Smoothing |
particle_smoother.nlg_ssm | Particle Smoothing |
particle_smoother.sde_ssm | 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.ar1 | Bayesian Inference of Linear-Gaussian State Space Models |
run_mcmc.bsm | Bayesian Inference of Linear-Gaussian State Space Models |
run_mcmc.gssm | Bayesian Inference of Linear-Gaussian State Space Models |
run_mcmc.lgg_ssm | Bayesian Inference of Linear-Gaussian State Space Models |
run_mcmc.ngssm | Bayesian inference of non-Gaussian or non-linear state space models using MCMC |
run_mcmc.ng_ar1 | Bayesian inference of non-Gaussian or non-linear state space models using MCMC |
run_mcmc.ng_bsm | Bayesian inference of non-Gaussian or non-linear state space models using MCMC |
run_mcmc.nlg_ssm | Bayesian inference of non-Gaussian or non-linear state space models using MCMC |
run_mcmc.sde_ssm | Bayesian inference of non-Gaussian or non-linear state space models using MCMC |
run_mcmc.svm | Bayesian inference of non-Gaussian or non-linear state space models using MCMC |
sde_ssm | Univariate state space model with continuous SDE dynamics |
sim_smoother | Simulation Smoothing |
smoother | Kalman Smoothing |
summary.mcmc_output | Summary of MCMC object |
svm | Stochastic Volatility Model |
ukf | Unscented Kalman Filtering |
uniform | Prior objects for bssm models |