bprobgHsCont

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), and Frank.

Routine for fitting bivariate models for binary and continuous responses with linear and nonlinear effects in the presence of associated error equations, endogeneity, non-random sample selection or partial observability.

bprobgHsCont: Internal Function

Description

Arguments