Internal Function

Description

It provides the log-likelihood, gradient and observed information matrix for penalized or unpenalized maximum likelihood optimization, when one response is binary and the other continuous. 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.

Author(s)

Maintainer: Giampiero Marra giampiero.marra@ucl.ac.uk

Results