Bayesian networks are a class of models that are widely used for the diagnosis, prediction, and risk assessment of complex operational systems. Multiple approaches, as well as implemented software, now guide their construction via learning from data or expert elicitation. However, current software only includes minimal functionalities to explore the assumptions, quality of fit, and sensitivity to learned parameters of a constructed Bayesian network. Here, we illustrate the usage of the bnmonitor R package: the first comprehensive software for model-checking of a Bayesian network. An applied data analysis using bnmonitor is carried out over a medical dataset to illustrate the use of its wide array of functions.