Simplifying Bayesian analysis of graphical models for the social sciences with easybgm: A user-friendly R-package

Abstract

Network psychometrics models psychological constructs as interconnected variables. Rather than treating variables as independent entities, network analysis views them as nodes in a system that interact with each other; their interactions yield partial associations. Recently, researchers have empha-sized the use of Bayesian methods in graphical modeling to accurately quan-tify uncertainty in the model and its parameters. Several R packages have been developed that implement different Bayesian estimation approaches for graphical modeling in R. However, they all require different inputs and pro-duce different outputs, making them difficult to use for applied researchers. In this paper, we present a user-friendly R package called easybgm that combines the powerful analysis tools into a cohesive package for applied re-searchers. The package allows researchers to fit any type of cross-sectional data, extract results, and visualize results such as network plots, edge evi-dence plots, and structure uncertainty plots. We introduce the package and demonstrate its use with two examples.