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bssm (version 0.1.8)

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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Install

install.packages('bssm')

Monthly Downloads

1,113

Version

0.1.8

License

GPL (>= 2)

Maintainer

Jouni Helske

Last Published

September 25th, 2019

Functions in bssm (0.1.8)

General multivariate linear-Gaussian state space models

General multivariate linear Gaussian state space models

Prior objects for bssm models

Log-likelihood of the State Space Model

print.mcmc_output

Print Results from MCMC Run

General univariate linear-Gaussian state space models

Bayesian Inference of Linear-Gaussian State Space Models

Bayesian Inference of State Space Models

Convert SSModel Object to gssm or ngssm Object

gaussian_approx

Gaussian approximation of non-Gaussian state space model

Simulated Poisson time series data

importance_sample

Importance Sampling from non-Gaussian State Space Model

predict.mcmc_output

Predictions for State Space Models

Pound/Dollar daily exchange rates

Expand the Jump Chain representation

summary.mcmc_output

Summary of MCMC object

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

Univariate state space model with continuous SDE dynamics

Simulation Smoothing

Stochastic Volatility Model

Kalman Smoothing

Kalman Filtering

Non-Gaussian model with AR(1) latent process

General multivariate nonlinear Gaussian state space models

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

Non-Gaussian Basic Structural (Time Series) Model

Unscented Kalman Filtering

particle_smoother

Particle Smoothing

Extended Kalman Particle Filtering

autoplot.predict_bssm

Plot predictions based on bssm package

Bayesian Inference of State Space Models

bootstrap_filter

Bootstrap Filtering

(Iterated) Extended Kalman Filtering

Extended Kalman Smoothing

Univariate Gaussian model with AR(1) latent process

Basic Structural (Time Series) Model

Deaths by drowning in Finland in 1969-2014