  Log-linear modeling is a popular method for the analysis of contingency table
data. When the table is sparse, and the data falls on a proper face $F$ of the
convex support, there are consequences on model inference and model selection.
Knowledge of the cells determining $F$ is crucial to mitigating these effects.
We introduce the R package (R Core Team (2016)) eMLEloglin for determining $F$
and passing that information on to the glm package to fit the model properly.
