bprobgHsCont

bprobgHsCont3

<p>It provides the log-likelihood, gradient and observed information matrix for 
penalized/unpenalized maximum likelihood optimization when copula models with binary and continuous margins are employed.</p>

Routines for fitting various joint regression models, with several types of covariate effects, in the presence of associated error equations, endogeneity, non-random sample selection or partial observability.

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bprobgHsCont: Internal Function

Description

Arguments