bprobSSH

It provides the log-likelihood, gradient and Hessian matrix for 
unpenalized maximum likelihood optimization, in the presence of non-random sample selection.

Routine for fitting bivariate probit models with
        semiparametric predictors (including linear, nonlinear and
        nonparametric random effects) in the presence of correlated
        error equations, endogeneity or sample selection.

bprobSSH: Internal Function

Description

Arguments