The R package easybgm provides a user-friendly package for
performing a Bayesian analysis of psychometric networks. In particular,
it helps to fit, extract, and visualize the results of a Bayesian
graphical model commonly used in the social and behavioral sciences. The
package is a wrapper around existing packages. So far, the package
supports fitting and extracting results of cross-sectional network
models using BDgraph (Mohammadi & Wit, 2015), BGGM(Williams &
Mulder, 2019), and bgms (Marsman, van den Bergh & Haslbeck, 2025). As
output, the package extracts the posterior parameter estimates, the
posterior inclusion probability, the inclusion Bayes factor, and
optionally posterior samples of the parameters and the nodes centrality.
The package comes with an extensive suite of visualization functions.
The package now also supports comparing networks across groups. This is
done through Bayesian inference to quantify differences in conditional
(in)dependence structures, using functionality from BGGM (Williams et
al., 2020) and bgms (Marsman et al., 2025). In addition, it allows
users to incorporate clustering assumptions by specifying a stochastic
block prior on the network structure and to test hypotheses about the
presence of such clustering (Sekulovski et al., 2025) via bgms.
To install this package from Github use
install.packages("remotes")
remotes::install_github("KarolineHuth/easybgm")To rather install the most up-to-date developer version, use
install.packages("remotes")
remotes::install_github("KarolineHuth/easybgm", ref = "developer")The package consists of wrapper functions around existing R packages
(i.e., BDgraph, bgms, and BGGM). To initiate estimation,
researchers must specify the data set and the data type (i.e.,
continuous, mixed, ordinal, or binary). Based on the data type
specification, easybgm estimates the network using the appropriate R
package (i.e., BDgraph for continuous and mixed data, and bgms for
ordinal and binary data). Users can override the default package
selection by specifying their preferred R package with the package
argument. All other arguments, such as package-specific informed prior
specifications, can be passed to easybgm. As output, easybgm returns
the posterior parameter estimates, the posterior inclusion probability,
and the inclusion Bayes factor. In addition, the package extracts the
posterior samples of the parameters by setting save = TRUE and the
strength centrality samples by setting centrality = TRUE. When
edge_prior = "Stochastic-Block" is used with bgms, the output
additionally includes the posterior estimates of node- cluster
memberships, the posterior inclusion probabilities for all possible
numbers of clusters, and the posterior coclustering matrix which depicts
the proportion of times each pair of nodes appeared in the same cluster.
The package comes with an extensive suite of functions to visualize the
results of the Bayesian analysis of networks. We provide more
information on each of the plots below. The visualization functions use
qgraph (Epskamp et al., 2012) or ggplot2 (Wickham, 2016) as the
backbone.
The edge evidence plot aids researchers in deciding which edges provide
robust inferential conclusions. In the edge evidence plot, edges
represent the inclusion Bayes factor
In the network plot, edges indicate the strength of partial association
between two nodes. The network plot shows all edges with an inclusion
Bayes factor greater than
The structure uncertainty can be assessed with the posterior structure probability plot and the posterior complexity plot. The posterior structure probability plot shows the posterior probabilities of the visited structures, sorted from the most to the least probable. Each dot represents one structure. The more structures with similar posterior probability, the more uncertain the true structure. If one structure dominates the posterior structure probability, we can be relatively certain about the true structure.
The posterior complexity plot shows the posterior probability of a structure complexity (i.e., number of present edges in a network). Here, the posterior probability of all structures with the same complexity are aggregated into one plot.
The 95 % highest density intervals (HDI) of the parameters are visualized with a parameter forest plot. In the plot, dots represent the median of the posterior samples and the lines indicate the shortest interval that covers 95% of the posterior distribution. The narrower an interval, the more stable a parameter.
Researchers often use centrality measures to obtain aggregated information for each node, such as the connectedness quantified by strength centrality. Credible intervals for strength centrality can be obtained by calculating the centrality measure for each sample of the posterior distribution. The higher the centrality, the more connected the node; error bars represent the 95% High Density Interval (HDI).
We want to illustrate the package use with an example. In particular, we
use the women and mathematics data which can be loaded with the package
BGGM. We fit the model and extract its results with the function
easybgm. We specify the data and the data type, which in this case is
binary.
library(easybgm)
library(BGGM)
data <- na.omit(women_math)
colnames(data) <- c("LCA", "GND", "SCT", "NMF", "SBP", "FTP")
res <- easybgm(data, type = "binary")Having fitted the model, we can now take a look at its results. When
edge_prior = "Stochastic-Block", the summary also includes clustering
information, such as the estimated node memberships, the posterior
probabilities for all possible numbers of clusters, and the Bayes factor
comparing the hypothesis that there are two or more clusters against the
null of a single cluster. Researchers can additionally use the
clusterBayesfactor function to test hypotheses about specific numbers
of clusters.
summary(res)
#> BAYESIAN ANALYSIS OF NETWORKS
#> Model type: binary
#> Number of nodes: 6
#> Fitting Package: bgms
#> ---
#> EDGE SPECIFIC OVERVIEW
#> Relation Estimate Posterior Incl. Prob. Inclusion BF Category
#> LCA-GND 0.000 0.034 0.035 excluded
#> LCA-SCT 0.001 0.027 0.028 excluded
#> GND-SCT 0.003 0.043 0.044 excluded
#> LCA-NMF 0.001 0.038 0.040 excluded
#> GND-NMF 0.508 1.000 Inf included
#> SCT-NMF -0.012 0.084 0.092 excluded
#> LCA-SBP 0.001 0.031 0.032 excluded
#> GND-SBP -0.756 1.000 Inf included
#> SCT-SBP 0.337 0.982 54.556 included
#> NMF-SBP -0.980 1.000 Inf included
#> LCA-FTP -0.004 0.051 0.054 excluded
#> GND-FTP 0.000 0.040 0.042 excluded
#> SCT-FTP 1.176 1.000 Inf included
#> NMF-FTP 0.670 1.000 Inf included
#> SBP-FTP -0.014 0.090 0.099 excluded
#>
#> Bayes Factors larger than 10 were considered sufficient evidence for the categorization.
#> ---
#> AGGREGATED EDGE OVERVIEW
#> Number of included edges: 6
#> Number of inconclusive edges: 0
#> Number of excluded edges: 9
#> Number of possible edges: 15
#>
#> ---
#> STRUCTURE OVERVIEW
#> Number of visited structures: 109
#> Number of possible structures: 32768
#> Posterior probability of most likely structure: 0.6264
#> ---Furthermore, we can visualize the results with plots. In a first step,
we assess the edge evidence plot in which edges represent the inclusion
Bayes factor split argument to TRUE. As such, the left plot shows
edges with some evidence for inclusion (i.e.,
plot_edgeevidence(res, edge.width = 2, split = F)
Furthermore, we can look at the network plot in which edges represent the partial associations.
plot_network(res, layout = "spring",
layoutScale = c(.8,1), palette = "R",
theme = "TeamFortress", vsize = 6)
We can also assess the structure specifically with three plots. Note
that this only works, if we use either the BDgraph or bgms package.
plot_structure_probabilities(res, as_BF = FALSE)
plot_complexity_probabilities(res, as_BF = FALSE)
plot_structure(res, layoutScale = c(.8,1), palette = "R",
theme = "TeamFortress", vsize = 6, edge.width = .3, layout = "spring")
In addition we can obtain posterior samples from the posterior
distribution by setting save = TRUE in the easybgm function and
thereby open up new possibilities of assessing the model. We can extract
the posterior density of the parameters with a parameter forest plot.
res <- easybgm(data, type = "binary", save = TRUE, centrality = TRUE)
plot_parameterHDI(res)
Furthermore, researcher can wish to aggregate the findings of the
network model, commonly done with centrality measures. Due to the
discussion around the meaningfulness of centrality measures in
psychometric network models, we recommend users to stick to the strength
centrality. To obtain the centrality measures, users need to set
save = TRUE and centrality = TRUE, when estimating the network model
with easybgm. The centrality measures can be inspected with the
centrality plot.
plot_centrality(res, measures = "Strength")
All of the functionalities mentioned above can also be used when
comparing networks across groups. The function easybgm_compare allows
users to compare networks across two or more groups using Bayesian
inference to extract differences in conditional (in)dependence
structures. The function supports the packages BGGM and bgms.
library(easybgm)
library(bgms)
data <- na.omit(ADHD)
group1 <- data[1:10, 1:5]
group2 <- data[11:20, 1:5]
# for two groups
res <- easybgm_compare(list(group1, group2),
type = "binary", save = TRUE)
# for multiple groups
res <- easybgm_compare(data[1:200, 1:5],
group_indicator = rep(c(1, 2, 3, 4), each = 50),
type = "binary",
save = TRUE,
)For more information on the package, the Bayesian background, its application to networks and the respective plots, check out:
Huth, K., Keetelaar, S., Sekulovski, N., van den Bergh, D., & Marsman, M. (2023). Simplifying Bayesian analysis of graphical models for the social sciences with easybgm: A user-friendly R-package. https://doi.org/10.31234/osf.io/8f72p
Huth, K., de Ron, J., Luigjes, J., Goudriaan, A., Mohammadi, R., van Holst, R., Wagenmakers, E.J., & Marsman, M. (2023). Bayesian Analysis of Cross-sectional Networks: A Tutorial in R and JASP. PsyArXiv https://doi.org/10.31234/osf.io/ub5tc.
If you encounter any bugs or have ideas for new features, you can submit
them by creating an issue on Github. Additionally, if you want to
contribute to the development of easybgm, you can initiate a branch
with a pull request; we can review and discuss the proposed changes.
Epskamp, S., Cramer, A. O. J., Waldorp, L. J., Schmittmann, V. D., & Borsboom, D. (2012). qgraph: Network Visualizations of Relationships in Psychometric Data. Journal of Statistical Software, 48. https://doi.org/10.18637/jss.v048.i04
Huth, K., de Ron, J., Luigjes, J., Goudriaan, A., Mohammadi, R., van Holst, R., Wagenmakers, E.J., & Marsman, M. (2023). Bayesian Analysis of Cross-sectional Networks: A Tutorial in R and JASP. PsyArXiv https://doi.org/10.31234/osf.io/ub5tc.
Marsman M, van den Bergh D, Haslbeck J.M.B. (2025) Bayesian Analysis of the Ordinal Markov Random Field. Psychometrika, 90(1):146-182. https://doi.org/10.1017/psy.2024.4
Mohammadi, Reza, and Ernst C Wit. (2015). BDgraph: An R Package for Bayesian Structure Learning in Graphical Models. Journal of Statistical Software, 89. https://doi.org/10.18637/jss.v089.i03.
Sekulovski, N., Arena, G., Haslbeck, J. M. B., Huth, K., Friel, N., & Marsman, M. (2025). A Stochastic Block Prior for Clustering in Graphical Models. PsyArXiv https://doi.org/10.31234/osf.io/29p3m_v1
Wickham, H. (2016). ggplot2: Elegant graphics for data analysis. Springer-Verlag New York. Retrieved from https://ggplot2.tidyverse.org
Williams, Donald R, and Joris Mulder (2019). Bayesian Hypothesis Testing for Gaussian Graphical Models: Conditional Independence and Order Constraints. PsyArXiv. https://doi.org/10.31234/osf.io/ypxd8.
Williams DR, Rast P, Pericchi LR, Mulder J (2020). Comparing Gaussian graphical models with the posterior predictive distribution and Bayesian model selection. Psychological Methods. https://doi.10.1037/met0000254.
