bprobgHs.NRE

It provides the log-likelihood, gradient and observed information matrix for 
penalized or unpenalized maximum likelihood optimization when the linear predictors include normally distributed
random effects. Possible bivariate distributions for the model equations are bivariate normal, Clayton, 
rotated Clayton (90 degrees), survival Clayton, rotated Clayton (270 degrees), Joe, rotated Joe (90 degrees), survival Joe, 
rotated Joe (270 degrees), Gumbel, rotated Gumbel (90 degrees), survival Gumbel, rotated Gumbel (270 degrees), and Frank.

Routine for fitting bivariate probit models with semiparametric predictors (including linear, nonlinear and random effects) in the presence of correlated error equations, endogeneity or sample selection. Bivariate copula models are also supported.

bprobgHs.NRE: Internal Function

Description

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