bprobgHsPO

bprobgHsPO0

It provides the log-likelihood, gradient and observed or expected information matrix for 
penalized/unpenalized maximum likelihood optimization when bivariate probit models with partial
observability are employed.

Routines for fitting various joint (and univariate) regression models, with several types of covariate effects, in the presence of equations' errors association, endogeneity, non-random sample selection or partial observability.

Giampiero Marra

GJRM

Generalised Joint Regression Modelling

bprobgHsPO function

Maintainer: Giampiero Marra <a href="/link/giampiero.marra%40ucl.ac.uk?package=GJRM&version=0.2-6.5" data-mini-rdoc="GJRM::giampiero.marra@ucl.ac.uk">giampiero.marra@ucl.ac.uk</a>

Author

Internal Function — bprobgHsPO

Maintainer: Giampiero Marra <a href='mailto:giampiero.marra@ucl.ac.uk'>giampiero.marra@ucl.ac.uk</a>

bprobgHsPO: Internal Function

Description

Arguments

Author