bprobgHsContSS

bprobgHsCont3SS

bprobgHsCont3binTWSS

It provides the log-likelihood, gradient and observed information matrix for 
penalized/unpenalized maximum likelihood optimization when copula sample selection models with continuous margins 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

bprobgHsContSS 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 — bprobgHsContSS

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

bprobgHsContSS: Internal Function

Description

Arguments

Author