Various parameters for fitting control of hurdle
model regression using hurdle.
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)shape parameter for gamma prior distributions.
rate parameter for gamma prior distributions.
size parameter for negative binomial likelihood distributions.
mu parameter for normal prior distributions.
standard deviation for normal prior distributions.
Markov-chain tuning for regression coefficient estimation.
Markov chain tuning for parameter estimation of 'extreme' observations distribution.
initial value for the poisson likelihood lambda parameter.
initial value for the negative binomial or log normal likelihood mu parameter.
initial value for the generalized pareto likelihood sigma parameter.
initial value for the generalized pareto likelihood xi parameter.
A list of all input values.