The difficulty of implementing versatile software for general classes of graphical models and the varying focus in different disciplines limit the applications of BNs compared to the state of the art in the literature. Nevertheless, the number of R packages for Bayesian networks has been slowly increasing in recent years. In this chapter we will provide an overview of available software packages, without pretending to be exhaustive, and we will introduce some classic R packages dealing with different aspects of learning and inference.