  Recent advances in computational methods for intractable models have made
network data increasingly amenable to statistical analysis. Exponential random
graph models (ERGMs) emerged as one of the main families of models capable of
capturing the complex dependence structure of network data in a wide range of
applied contexts. The Bergm package for R has become a popular package to carry
out Bayesian parameter inference, missing data imputation, model selection and
goodness-of-fit diagnostics for ERGMs. Over the last few years, the package has
been considerably improved in terms of efficiency by adopting some of the
state-of-the-art Bayesian computational methods for doubly-intractable
distributions. Recently, version 5 of the package has been made available on
CRAN having undergone a substantial makeover, which has made it more accessible
and easy to use for practitioners. New functions include data augmentation
procedures based on the approximate exchange algorithm for dealing with missing
data, adjusted pseudo-likelihood and pseudo-posterior procedures, which allow
for fast approximate inference of the ERGM parameter posterior and model
evidence for networks on several thousands nodes.
