R
Definition
R is a statistical computing environment widely used in psychology and the social sciences for fitting, estimating, and visualizing complex quantitative models. In network psychometrics, several R packages implement distinct approaches to graphical modeling, including frequentist tools such as bootnet, qgraph, and igraph, as well as Bayesian packages such as bgms, BDgraph, and BGGM, each requiring different inputs and producing different outputs. The easybgm package was developed specifically to consolidate these Bayesian tools into a single, accessible workflow, allowing applied researchers with limited programming experience to fit models to cross-sectional data, extract posterior estimates, and generate publication-ready visualizations. Because the existing Bayesian packages were aimed primarily at statistically advanced users, easybgm lowers the barrier to entry by wrapping their functionality and providing educational vignettes alongside the core estimation routines.
Sources: Huth et al. (2024)
Related Terms
- network psychometrics (1 shared article)
- Bayesian inference (1 shared article)
- tutorial (1 shared article)
Applications
R and Bayesian Graphical Modeling
Bayesian graphical modeling in R involves estimating both the structure of a network, meaning which edges are present or absent, and the partial association parameters encoded in the edge weight matrix. The easybgm package unifies three existing R packages for this purpose, bgms, BDgraph, and BGGM, enabling researchers to apply Bayesian model-averaging and compute inclusion Bayes factors without switching between incompatible interfaces.
Sources: Huth et al. (2024)
R and Network Psychometrics
Network psychometrics treats psychological variables as nodes in a system whose pairwise conditional dependencies are estimated through graphical models. R has become the primary computational platform for this work, hosting the packages that implement both frequentist and Bayesian estimation of such networks for data types ranging from binary and ordinal to continuous.
Sources: Huth et al. (2024)
