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bssm (version 1.1.7-1)

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

Description

Efficient methods for Bayesian inference of state space models via particle Markov chain Monte Carlo (MCMC) and MCMC based on parallel importance sampling type weighted estimators (Vihola, Helske, and Franks, 2020, ). Gaussian, Poisson, binomial, negative binomial, and Gamma observation densities and basic stochastic volatility models with linear-Gaussian state dynamics, as well as general non-linear Gaussian models and discretised diffusion models are supported.

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Install

install.packages('bssm')

Monthly Downloads

1,123

Version

1.1.7-1

License

GPL (>= 2)

Issues

1

Pull Requests

0

Stars

43

Forks

14

Maintainer

Jouni Helske

Last Published

September 21st, 2021

Functions in bssm (1.1.7-1)

bsm_ng

Non-Gaussian Basic Structural (Time Series) Model
bsm_lg

Basic Structural (Time Series) Model
uniform_prior

Prior objects for bssm models
bootstrap_filter

Bootstrap Filtering
ar1_ng

Non-Gaussian model with AR(1) latent process
ar1_lg

Univariate Gaussian model with AR(1) latent process
as.data.frame.mcmc_output

Convert MCMC chain to data.frame
as_draws_df.mcmc_output

Convert run_mcmc output to draws_df format
bssm

Bayesian Inference of State Space Models
as_bssm

Convert KFAS Model to bssm Model
expand_sample

Expand the Jump Chain representation
ekpf_filter

Extended Kalman Particle Filtering
gaussian_approx

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

Importance Sampling from non-Gaussian State Space Model
predict.mcmc_output

Predictions for State Space Models
kfilter

Kalman Filtering
print.mcmc_output

Print Results from MCMC Run
sim_smoother

Simulation Smoothing
run_mcmc

Bayesian Inference of State Space Models
post_correct

Run Post-correction for Approximate MCMC using \(\psi\)-APF
exchange

Pound/Dollar daily exchange rates
fast_smoother

Kalman Smoothing
ssm_mlg

General multivariate linear Gaussian state space models
cpp_example_model

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

General univariate non-Gaussian state space model
poisson_series

Simulated Poisson time series data
ssm_mng

General Non-Gaussian State Space Model
summary.mcmc_output

Summary of MCMC object
drownings

Deaths by drowning in Finland in 1969-2019
svm

Stochastic Volatility Model
ukf

Unscented Kalman Filtering
suggest_N

Suggest Number of Particles for \(\psi\)-APF Post-correction
ssm_nlg

General multivariate nonlinear Gaussian state space models
ekf_smoother

Extended Kalman Smoothing
ssm_ulg

General univariate linear-Gaussian state space models
particle_smoother

Particle Smoothing
ssm_sde

Univariate state space model with continuous SDE dynamics
ekf

(Iterated) Extended Kalman Filtering
logLik.gaussian

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