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ar1_ng

Non-Gaussian model with AR(1) latent process

Constructs a simple non-Gaussian model where the state dynamics follow an AR(1) process.

ar1_ng(y, rho, sigma, mu, distribution, phi, u = 1, beta, xreg = NULL)

`y`	Vector or a `ts` object of observations.
`rho`	prior for autoregressive coefficient.
`sigma`	Prior for the standard deviation of noise of the AR-process.
`mu`	A fixed value or a prior for the stationary mean of the latent AR(1) process. Parameter is omitted if this is set to 0.
`distribution`	Distribution of the observed time series. Possible choices are `"poisson"`, `"binomial"`, `"gamma"`, and `"negative binomial"`.
`phi`	Additional parameter relating to the non-Gaussian distribution. For negative binomial distribution this is the dispersion term, for gamma distribution this is the shape parameter, and for other distributions this is ignored.
`u`	Constant parameter vector for non-Gaussian models. For Poisson, gamma, and negative binomial distribution, this corresponds to the offset term. For binomial, this is the number of trials.
`beta`	Prior for the regression coefficients.
`xreg`	Matrix containing covariates.

Object of class ar1_ng.

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

v1.1.4

GPL (>= 2)

Authors

Jouni Helske [aut, cre] (<https://orcid.org/0000-0001-7130-793X>), Matti Vihola [aut] (<https://orcid.org/0000-0002-8041-7222>)

Initial release

2021-04-13