bitsgHs

It provides the quantites needed to calculate the log-likelihood, gradient and Hessian matrix for 
penalized or unpenalized maximum likelihood optimization, for a number of copula models.

Routine for fitting continuous or discrete response copula sample selection models with semi-parametric predictors, including linear and nonlinear effects.

Giampiero Marra

bitsgHs: Internal Function

Description

Arguments