hurdle_control: Control Parameters for Hurdle Model Count Data Regression
In hurdlr: Zero-Inflated and Hurdle Modelling Using Bayesian Inference

Description Usage Arguments Value Author(s) See Also

Various parameters for fitting control of hurdle model regression using hurdle.

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hurdle_control(a = 1, b = 1, size = 1, beta.prior.mean = 0,
  beta.prior.sd = 1000, beta.tune = 1, pars.tune = 0.2, lam.start = 1,
  mu.start = 1, sigma.start = 1, xi.start = 1)

`a`	shape parameter for gamma prior distributions.
`b`	rate parameter for gamma prior distributions.
`size`	size parameter for negative binomial likelihood distributions.
`beta.prior.mean`	mu parameter for normal prior distributions.
`beta.prior.sd`	standard deviation for normal prior distributions.
`beta.tune`	Markov-chain tuning for regression coefficient estimation.
`pars.tune`	Markov chain tuning for parameter estimation of 'extreme' observations distribution.
`lam.start`	initial value for the poisson likelihood lambda parameter.
`mu.start`	initial value for the negative binomial or log normal likelihood mu parameter.
`sigma.start`	initial value for the generalized pareto likelihood sigma parameter.
`xi.start`	initial value for the generalized pareto likelihood xi parameter.