adinar: AdINAR model training

In bayesianfactory/BayesINAR: BayesINAR model package

Description

Computes the posterior distribution for the DP-INAR family using a Gibbs sampler.

Usage

adinar(time_series, p = 1, prior = list(a_alpha = NULL, a_lambda =
  NULL, b_lambda = NULL, a_theta = NULL, b_theta = NULL, a_w = NULL, b_w =
  NULL), burn_in = 10^3, chain_length = 10^4, random_seed = 1761,
  verbose = TRUE)

Arguments

time_series	A univariate time series.
prior	List of prior hyperparameters where: a_alpha Hyperparameters of the thinning component. a_lambda, b_lambda Hyperparameters of the Gamma prior for the Poisson innovation rate. a_theta, b_theta Hyperparameters of the Beta prior for the Geometric parameter. a_w, b_w Hyperparameter of the Beta prior for the Geometric-Poisson weight mixture.
burn_in	Number of iterations for the "burn-in" period which are discarded in the chain.
chain_length	Number of iterations of the chain.
random_seed	Value of the random seed generator.
verbose	If TRUE log info is provided.

Value

adinar returns an object of class "adinar".

bayesianfactory/BayesINAR documentation built on Dec. 16, 2019, 12:38 a.m.