  In this paper we briefly review the main methodological aspects concerned
with the application of the Bayesian approach to model choice and model
averaging in the context of variable selection in regression models. This
includes prior elicitation, summaries of the posterior distribution and
computational strategies. We then examine and compare various publicly
available {\tt R}-packages for its practical implementation summarizing and
explaining the differences between packages and giving recommendations for
applied users. We find that all packages reviewed lead to very similar results,
but there are potentially important differences in flexibility and efficiency
of the packages.
