bprobgHsDiscr1

bprobgHsDiscr2

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

bprobgHsDiscr1: Internal Function

Description

Arguments