Bayesian estimation and edge selection for graphical models of mixed binary, ordinal, and continuous variables. The variable types determine the model: an ordinal Markov random field for discrete data, a Gaussian graphical model for continuous data, or a mixed Markov random field combining both. Edge inclusion is determined through spike-and-slab priors, yielding posterior inclusion probabilities for each edge. Supports multi-group comparison via bgmCompare()', simulation, prediction, and missing data imputation.