bssm2.0.2 package

Univariate Gaussian model with AR(1) latent process

Non-Gaussian model with AR(1) latent process

Convert MCMC Output to data.frame

Convert KFAS Model to bssm Model

Convert run_mcmc Output to draws_df Format

Asymptotic Variance of IS-type Estimators

Bootstrap Filtering

Basic Structural (Time Series) Model

Non-Gaussian Basic Structural (Time Series) Model

Bayesian Inference of State Space Models

Prior objects for bssm models

Quick Diagnostics Checks for run_mcmc Output

Example C++ Codes for Non-Linear and SDE Models

(Iterated) Extended Kalman Filtering

Extended Kalman Smoothing

Extended Kalman Particle Filtering

Effective Sample Size for IS-type Estimators

Expand the Jump Chain representation

Fitted for State Space Model

Gaussian Approximation of Non-Gaussian/Non-linear State Space Model

Integrated Autocorrelation Time

Importance Sampling from non-Gaussian State Space Model

Kalman Filtering

Extract Log-likelihood of a State Space Model of class bssm_model

Particle Smoothing

Trace and Density Plots for mcmc_output

Run Post-correction for Approximate MCMC using $\psi$-APF

Predictions for State Space Models

Print Results from MCMC Run

Bayesian Inference of State Space Models

Simulation Smoothing

Kalman Smoothing

General multivariate linear Gaussian state space models

General Non-Gaussian State Space Model

General multivariate nonlinear Gaussian state space models

Univariate state space model with continuous SDE dynamics

General univariate linear-Gaussian state space models

General univariate non-Gaussian state space model

Suggest Number of Particles for $\psi$-APF Post-correction

Summary Statistics of Posterior Samples

Stochastic Volatility Model

Unscented Kalman Filtering