bprobgHsPO

It provides the log-likelihood, gradient and observed or expected information matrix for 
penalized or unpenalized maximum likelihood optimization for the case of bivariate models with partial
observability. Possible bivariate distributions 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 models for binary responses with semiparametric predictors (including linear and nonlinear effects) in the presence of correlated error equations, endogeneity, non-random sample selection or partial observability.

bprobgHsPO: Internal Function

Description

Arguments