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Description: Network psychometrics is a cutting-edge approach to studying psychological constructs as interconnected variables. Rather than treating variables as independent entities, network analysis or graphical modeling views them as nodes in a system that interact with each other; its interactions yield partial associations. Recently, researchers have emphasized the use of Bayesian methods in graphical modeling to accurately quantify uncertainty in the model and its parameters. Several R-packages have been developed that implement different Bayesian estimation approaches to graphical modeling in R. However, they all require different input sets and produce varying outputs, making them difficult to use for applied researchers. In this paper, we introduce a user-friendly R-package called easybgm that aggregates the powerful analysis tools into one cohesive package aimed at applied researchers. The package allows researchers to fit cross-sectional data of any type, extract results, and visualize findings such as network plots, edge evidence plots, and structure uncertainty plots. We introduce the package and demonstrate its application with a worked-out example of women and mathematics.