bprobgHsSS

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

bprobgHsSS: Internal Function

Description

Arguments