bprobgHsSS: Internal Function
Description
It provides the log-likelihood, gradient and observed or expected information matrix for
penalized or unpenalized maximum likelihood optimization, for the non-random sample selection model case. 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), Frank, FGM, AMH.