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bssm (version 0.1.11)

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 and parallel importance sampling type weighted Markov chain Monte Carlo (Vihola, Helske, and Franks, 2017, ). Gaussian, Poisson, binomial, or negative binomial observation densities and basic stochastic volatility models with Gaussian state dynamics, as well as general non-linear Gaussian models and discretised diffusion models are supported.

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Version

Install

install.packages('bssm')

Monthly Downloads

1,113

Version

0.1.11

License

GPL (>= 2)

Maintainer

Jouni Helske

Last Published

February 26th, 2020

Functions in bssm (0.1.11)

importance_sample

Importance Sampling from non-Gaussian State Space Model
svm

Stochastic Volatility Model
run_mcmc.gssm

Bayesian Inference of Linear-Gaussian State Space Models
run_mcmc

Bayesian Inference of State Space Models
expand_sample

Expand the Jump Chain representation
exchange

Pound/Dollar daily exchange rates
summary.mcmc_output

Summary of MCMC object
kfilter

Kalman Filtering
predict.mcmc_output

Predictions for State Space Models
run_mcmc.ngssm

Bayesian inference of non-Gaussian or non-linear state space models using MCMC
poisson_series

Simulated Poisson time series data
mv_gssm

General multivariate linear-Gaussian state space models
as_gssm

Convert SSModel Object to gssm or ngssm Object
sde_ssm

Univariate state space model with continuous SDE dynamics
logLik.gssm

Log-likelihood of the State Space Model
ng_bsm

Non-Gaussian Basic Structural (Time Series) Model
lgg_ssm

General multivariate linear Gaussian state space models
ngssm

General univariate non-Gaussian/non-linear state space models
nlg_ssm

General multivariate nonlinear Gaussian state space models
gaussian_approx

Gaussian approximation of non-Gaussian state space model
gssm

General univariate linear-Gaussian state space models
ng_ar1

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

Unscented Kalman Filtering
particle_smoother

Particle Smoothing
print.mcmc_output

Print Results from MCMC Run
uniform

Prior objects for bssm models
fast_smoother

Kalman Smoothing
sim_smoother

Simulation Smoothing
bssm

Bayesian Inference of State Space Models
bsm

Basic Structural (Time Series) Model
autoplot.predict_bssm

Plot predictions based on bssm package
bootstrap_filter

Bootstrap Filtering
ekf

(Iterated) Extended Kalman Filtering
ekf_smoother

Extended Kalman Smoothing
ekpf_filter

Extended Kalman Particle Filtering
drownings

Deaths by drowning in Finland in 1969-2014
ar1

Univariate Gaussian model with AR(1) latent process