Methods | Special Issue: Network Psychometrics

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

Karoline B. S. Huth

, Sara Keetelaar

, Nikola Sekulovski

, Don van den Bergh

, & Maarten Marsman

https://doi.org/10.56296/aip00010
Published: March 15, 2024
Copyright: The authors (CC BY 4.0)

Huth, K.B.S., Keetelaar, S., Sekulovski, N., van den Bergh, D., & Marsman, M. (2024). Simplifying Bayesian analysis of graphical models for the social sciences with easybgm: A user-friendly R-package. advances.in/psychology, 2, e66366. https://doi.org/10.56296/aip00010

Huth, Karoline B. S., et al. "Simplifying Bayesian analysis of graphical models for the social sciences with easybgm: A user-friendly R-package." advances.in/psychology, vol. 2, no. 1, 2024, e66366. https://doi.org/10.56296/aip00010.

Huth, Karoline B. S., Sara Keetelaar, Nikola Sekulovski, Don van den Bergh, and Maarten Marsman. 2024. "Simplifying Bayesian analysis of graphical models for the social sciences with easybgm: A user-friendly R-package." advances.in/psychology 2 (1): e66366. https://doi.org/10.56296/aip00010.

Huth KBS, Keetelaar S, Sekulovski N, van den Bergh D, Marsman M. Simplifying Bayesian analysis of graphical models for the social sciences with easybgm: A user-friendly R-package. advances.in/psychology. 2024;2(1):e66366. doi:10.56296/aip00010.

Huth, K.B.S. et al. (2024) 'Simplifying Bayesian analysis of graphical models for the social sciences with easybgm: A user-friendly R-package', advances.in/psychology, 2(1), e66366. Available at: https://doi.org/10.56296/aip00010.

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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 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 for graphical modeling in R. However, they all require different inputs and produce 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 findings with network, edge evidence, and structure uncertainty plots. We introduce the package and demonstrate its use with two examples.

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