bprobgHsContUniv

bprobgHsContUniv3

bprobgHsContUnivBIN

bcontSurvGunivInform

bcontSurvGunivI

bcontSurvGunivL

bcontSurvGunivMIXED

bcontSurvGunivMIXED_ExcessHazard

bcontSurvGunivL_ExcessHazard

bcontSurvGuniv_ExcessHazard

bcontSurvGunivI_ExcessHazard

bcontSurvGunivMIXED_LeftTruncation

bcontSurvGunivMIXED_ExcessHazard_LeftTruncation

bcontSurvG_extended

It provides the log-likelihood, gradient and observed information matrix for 
penalized/unpenalized maximum likelihood optimization when fitting univariate models with discrete/continuous response.

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

bprobgHsContUniv 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 — bprobgHsContUniv

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

Internal Function

bprobgHsContUniv: Internal Function

Description

Arguments

Author