  Undirected graphical models are a key component in the analysis of complex
observational data in a large variety of disciplines. In many of these
applications one is interested in estimating the undirected graphical model
underlying a distribution over variables with different domains. Despite the
pervasive need for such an estimation method, to date there is no such method
that models all variables on their proper domain. We close this methodological
gap by combining a new class of mixed graphical models with a structure
estimation approach based on generalized covariance matrices. We report the
performance of our methods using simulations, illustrate the method with a
dataset on Autism Spectrum Disorder (ASD) and provide an implementation as an
R-package.
