Abstract Interest in log-linear modeling for social-network data has grown steadily since Holland and Leinhardt (1981) proposed their p 1 model. That model was designed for a single binary relationship (directed graph) representing interactions between individuals. It assumed that interactions between pairs of individuals are mutually independent. Subsequent work has extended the model in various ways, including block-modeling and the case of dependence between pairs of individuals. In empirical work it would often be desirable to fit a wide variety of these models, as the differences in predictions or goodness of fit are likely to provide insights into the data. This has not been common practice, however, because estimation for some of the models has been difficult, and the maximum likelihood schemes developed for others involve different computer programs not always available in standard packages. The focus of this article is on a general estimation technique that maximizes the pseudolikelihood, the pro...