bprobgHs

bprobgHstwoParC

bprobgHsBinROY

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

bprobgHs 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 — bprobgHs

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

bprobgHs: Internal Function

Description

Arguments

Author