bprobgHsPO

It provides the log-likelihood, gradient and observed or expected information matrix for 
penalized or unpenalized maximum likelihood optimization for the case of bivariate normal models with partial
observability.

Routine for fitting bivariate models for binary and continuous responses with linear and nonlinear effects in the presence of associated error equations, endogeneity, non-random sample selection or partial observability.

bprobgHsPO: Internal Function

Description

Arguments