Bnlearn provides tools for learning the graphical structure of Bayesian networks from data using various structure learning algorithms. It includes functionality for parameter learning after structure discovery, performing inferences on the learned networks, and generating samples via methods like Gibbs sampling. The package supports both constraint-based and score-based approaches to discovering causal relationships in data.