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Bayesian Inference of Non-Linear and Non-Gaussian State Space Models

Vignettes

- README.md
- bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in R"
- Diffusion models with bssm
- Non-linear models with bssm
- $\\psi$-APF for non-linear Gaussian state space models

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**ar1_lg:**Univariate Gaussian model with AR(1) latent process**ar1_ng:**Non-Gaussian model with AR(1) latent process**as_bssm:**Convert KFAS Model to bssm Model**as.data.frame.mcmc_output:**Convert MCMC Output to data.frame**as_draws-mcmc_output:**Convert 'run_mcmc' Output to 'draws_df' Format**asymptotic_var:**Asymptotic Variance of IS-type Estimators**bootstrap_filter:**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**bssm_prior:**Prior objects for bssm models**check_diagnostics:**Quick Diagnostics Checks for 'run_mcmc' Output**cpp_example_model:**Example C++ Codes for Non-Linear and SDE 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**estimate_ess:**Effective Sample Size for IS-type Estimators**exchange:**Pound/Dollar daily exchange rates**expand_sample:**Expand the Jump Chain representation**fitted.mcmc_output:**Fitted for State Space Model**gaussian_approx:**Gaussian Approximation of Non-Gaussian/Non-linear State Space...**iact:**Integrated Autocorrelation Time**importance_sample:**Importance Sampling from non-Gaussian State Space Model**kfilter:**Kalman Filtering**logLik_bssm:**Extract Log-likelihood of a State Space Model of class...**negbin_model:**Estimated Negative Binomial Model of Helske and Vihola (2021)**negbin_series:**Simulated Negative Binomial Time Series Data**particle_smoother:**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**sim_smoother:**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 Statistics of Posterior Samples**svm:**Stochastic Volatility Model**ukf:**Unscented Kalman Filtering**Browse all...**

```
library("testthat")
test_check("bssm")
```

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