bprobgHs: Internal Function
In egeminiani/GJRM: Generalised Joint Regression Modelling
Description
It provides the log-likelihood, gradient and observed/Fisher information matrix for
penalized/unpenalized maximum likelihood optimization when copula models with binary outcomes are employed.
Author(s)
Maintainer: Giampiero Marra giampiero.marra@ucl.ac.uk
egeminiani/GJRM documentation built on Sept. 1, 2020, 6:41 p.m.
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Description
It provides the log-likelihood, gradient and observed/Fisher information matrix for penalized/unpenalized maximum likelihood optimization when copula models with binary outcomes are employed.
Author(s)
Maintainer: Giampiero Marra giampiero.marra@ucl.ac.uk
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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