Type:	Package
Title:	Extracting and Visualizing Bayesian Graphical Models
Version:	0.3.1
Maintainer:	Karoline Huth <k.huth@uva.nl>
Description:	Fit and visualize the results of a Bayesian analysis of networks commonly found in psychology. The package supports fitting cross-sectional network models fitted using the packages 'BDgraph', 'bgms' and 'BGGM', as well as network comparison fitted using the 'bgms' and 'BBGM'. The package provides the parameter estimates, posterior inclusion probabilities, inclusion Bayes factor, and the posterior density of the parameters. In addition, for 'BDgraph' and 'bgms' it allows to assess the posterior structure space. Furthermore, the package comes with an extensive suite for visualizing results.
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL:	https://github.com/KarolineHuth/easybgm
BugReports:	https://github.com/KarolineHuth/easybgm/issues
Encoding:	UTF-8
RoxygenNote:	7.3.3
Depends:	R (≥ 4.1.0)
Imports:	BDgraph, BGGM, bgms (≥ 0.1.4), dplyr, ggplot2, HDInterval, igraph, qgraph, coda
Suggests:	testthat (≥ 3.2.3), vdiffr
Config/testthat/edition:	3
NeedsCompilation:	no
Packaged:	2025-12-08 11:37:52 UTC; karolinehuth
Author:	Karoline Huth [aut, cre], Sara Keetelaar [ctb], Nikola Sekulovski [ctb], Gali Geller [ctb]
Repository:	CRAN
Date/Publication:	2025-12-08 12:10:07 UTC

Plot of interaction parameters and their 95% highest density intervals

Description

Plots the 95% highest density interval of the posterior distribution of the parameter estimates. The plot can be used to visualize the uncertainty of the partial association estimates. The x-axis indicates the strength of the partial association. The y-axis indicates the edge between nodes $i$ and $j$. The farther the posterior estimates (i.e., the points in the plot) are from zero, the stronger the partial association of the edge. The wider the highest density intervals (i.e., the error bar around the point), the less certain we are about the strength of the association.

Usage

plot_parameterHDI(output, ...)

Arguments

Value

Returns a plot

Examples

library(easybgm)
library(bgms)


data <- na.omit(Wenchuan)
fit <- easybgm(data, type = "ordinal",
              iter = 100,  # for demonstration only (> 5e4 recommended)
              edge_selection = TRUE, save = TRUE)
plot_parameterHDI(fit)

Extract the results of a Bayesian analysis of networks

Description

Extract the results of a Bayesian analysis of networks

Usage

bgm_extract(fit, ...)

Arguments

Fit a Bayesian analysis of networks

Description

Fit a Bayesian analysis of networks

Usage

bgm_fit(fit, ...)

Arguments

Plot strength centralities and 95% highest density interval

Description

Visualize the strength centralities and their uncertainties. The centrality estimate can be obtained for each sample of the posterior distribution of the association parameters to obtain an estimate of the uncertainty of the strength centrality estimate.

Usage

plot_centrality(output, group_names = NULL, ...)

Arguments

Value

Returns a plot

Examples

library(easybgm)
library(bgms)

data <- na.omit(Wenchuan)
fit <- easybgm(data, type = "ordinal",
                iter = 100,  # for demonstration only (> 5e4 recommended)
                edge_selection = TRUE, save = TRUE, 
                centrality = TRUE)

plot_centrality(fit)

Calculate Clustering Bayes Factors for when using the Stochastic Block Model as an edge prior

Description

This function calculates Bayes factors to evaluate evidence in favor of clustering for models fitted with the bgms package (i.e., with arguments package = "bgms" and edge_prior = "Stochastic-Block" within the easybgm function). The function supports two types of Bayes factors: Bayes factors between two point hypothesized number of clusters (b1 and b2), and Bayes factor of the hypothesis of clustering (i.e., the complement hypothesis) against the hypothesis of no clustering (i.e., the null, which simply means that the network exibits one global cluster).

Usage

clusterBayesfactor(fit, type = "complement", b1 = NULL, b2 = NULL)

Arguments

Value

A numeric value representing the Bayes factor. When type is "point", the Bayes factor represents evidence in favor of b1 clusters against b2 clusters. When type is "complement", the Bayes factor represents evidence in favor of clustering (i.e., more than one cluster) against no clustering.

Plot posterior complexity probabilities

Description

Plots the posterior complexity probabilities of all visited structures, where complexity comprises the network density.

Usage

plot_complexity_probabilities(output, ...)

Arguments

Value

Returns a plot

Examples

library(easybgm)
library(bgms)

data <- na.omit(Wenchuan)
fit <- easybgm(data, type = "ordinal", save = TRUE, edge_selection = TRUE,
                iter = 100  # for demonstration only (> 5e4 recommended)
                )

plot_complexity_probabilities(fit)

Fit a Bayesian analysis of networks

Description

Easy estimation of a Bayesian analysis of networks to obtain conditional (in)dependence relations between variables in a network.

Usage

easybgm(
  data,
  type,
  package = NULL,
  not_cont = NULL,
  iter = 10000,
  save = FALSE,
  centrality = FALSE,
  progress = TRUE,
  posterior_method = "model-averaged",
  ...
)

Arguments

Details

Users may oftentimes wish to deviate from the default, usually uninformative, prior specifications of the packages to informed priors. This can be done by simply adding additional arguments to the easybgm function. Depending on the package that is running the underlying network estimation, researcher can specify different prior arguments. We give an overview of the prior arguments per package below.

bgms:

• interaction_scale the scale of the Cauchy distribution that is used as a prior for the pairwise interaction parameters. The default is 2.5.

• edge_prior prior on the graph structure, which can be either "Bernoulli", "Beta-Bernoulli" or "Stochastic Block". The default is "Bernoulli".

• inclusion_probability prior edge inclusion probability for the "Bernoulli" distribution. The default is 0.5.

• beta_bernoulli_alpha and beta_bernoulli_beta the parameters of the "Beta-Bernoulli" or "Stochastic Block" priors. The default is 1 for both.

• beta_bernoulli_alpha_between and beta_bernoulli_beta_between the parameters of the "Stochastic Block" prior for edges between blocks. This is currently only available in a developer version of bgms and will be available in version 0.1.6.2 or higher.

• dirichlet_alpha The shape of the Dirichlet prior on the node-to-block allocation parameters for the Stochastic Block prior on the graph structure.

• threshold_alpha and threshold_beta the parameters of the beta-prime distribution for the threshold parameters. The defaults are both set to 1.

• variable_type What kind of variables are there in x? Can be a single character string specifying the variable type of all p variables at once or a vector of character strings of length p specifying the type for each variable in x separately. Currently, bgm supports ⁠ordinal'' and ⁠blume-capel”. Binary variables are automatically treated as “ordinal’’. Defaults to variable_type = "ordinal".

BDgraph:

• df.prior prior on the parameters (i.e., inverse covariance matrix), degrees of freedom of the prior G-Wishart distribution. The default is set to 3.

• g.prior prior probability of edge inclusion. This can be either a scalar, if it is the same for all edges, or a matrix, if it should be different among the edges. The default is set to 0.5.

BGGM:

• prior_sd the standard deviation of the prior distribution of the interaction parameters, approximately the scale of a beta distribution. The default is 0.25.

We would always encourage researcher to conduct prior robustness checks.

Value

The returned object of easybgm contains several elements:

• parameters A p x p matrix containing partial associations.

• inc_probs A p x p matrix containing the posterior inclusion probabilities.

• BF A p x p matrix containing the posterior inclusion Bayes factors.

• structure Adjacency matrix of the median probability model (i.e., edges with a posterior probability larger 0.5).

In addition, for BDgraph and bgms, the function returns:

• structure_probabilities A vector containing the posterior probabilities of all visited structures, between 0 and 1.

• graph_weights A vector containing the number of times a particular structure was visited.

• sample_graphs A vector containing the indexes of a particular structure.

  For the bgms package, when edge_prior = "Stochastic-Block", the function will also return an object sbm which contains:

  • posterior_num_blocks A data frame with the estimated posterior probability of the possible number of clusters.

  • posterior_mean_allocations The posterior mean of the cluster assignments of the nodes.

  • posterior_mode_allocations The posterior mode of the cluster assignments of the nodes.

  • posterior_mean_coclustering_matrix A p x p matrix containing the estimated pairwise proportions of cluster occurrence of every variable. This matrix can be plotted to visually inspect the estimated number of clusters and visually inspect nodes that tend to switch clusters.

If using version 0.1.6.1 or higher of the bgms package, the function also returns the the Gelman-Rubin convergence statistic for each edge weight parameter. As well as the 95% Monte Carlo confidence interval for the inclusion Bayes factor.

For all packages, when setting save = TRUE and centrality = TRUE, the function will return the following objects respectively:

• samples_posterior A k x iter matrix containing the posterior samples for each parameter (i.e., k = (p/(p-1))/2) at each iteration (i.e., iter) of the sampler.

• centrality A p x iter matrix containing the centrality of a node at each iteration of the sampler.

Examples

library(easybgm)
library(bgms)

data <- na.omit(Wenchuan)

# Fitting the Wenchuan PTSD data

fit <- easybgm(data, type = "continuous",
                iter = 100 # for demonstration only (> 5e4 recommended)
                )

summary(fit)


# To extract the posterior parameter distribution
# and centrality measures

fit <- easybgm(data, type = "continuous",
                iter = 100, 
                centrality = TRUE, save = TRUE)

Compare networks across groups using Bayesian inference

Description

Easy comparison of networks using Bayesian inference to extract differences in conditional (in)dependence across groups.

Usage

easybgm_compare(
  data,
  type,
  package = NULL,
  not_cont = NULL,
  group_indicator = NULL,
  iter = 10000,
  save = TRUE,
  progress = TRUE,
  ...
)

Arguments

Details

Users may oftentimes wish to deviate from the default, usually uninformative, prior specifications of the packages to informed priors. This can be done by simply adding additional arguments to the easybgm function. Depending on the package that is running the underlying network estimation, researcher can specify different prior arguments. Please consult the original packages "bgms" and "BGGM" for the specific informed prior options.

We always encourage researcher to conduct prior robustness checks.

Value

The returned object of easybgm contains several elements:

• parameters A p x p matrix containing difference across partial associations.

• inc_probs A p x p matrix containing the posterior inclusion probabilities of subgroup differences.

• inc_BF A p x p matrix containing the posterior inclusion Bayes factors of subgroup differences.

• structure Adjacency matrix of the median probability model (i.e., edges with a posterior probability larger 0.5).

In addition, for bgms, the function returns:

• structure_probabilities A vector containing the posterior probabilities of all visited structures, between 0 and 1.

• graph_weights A vector containing the number of times a particular structure was visited.

• sample_graph A vector containing the indexes of a particular structure.

• convergence_parameter A vector containing the R-hat (Gelman–Rubin) statistic for the difference parameter measuring how well MCMC chains have converged to the same target distribution.

For both packages, when setting save = TRUE, the function will also return the following object:

• samples_posterior A k x iter matrix containing the posterior samples of parameter differences (i.e., k = (p/(p-1))/2) at each iteration (i.e., iter) of the sampler.

Examples

library(easybgm)
library(bgms)

data <- na.omit(ADHD)

group1 <- data[1:10, 1:3]
group2 <- data[11:20, 1:3]

# Fitting the Wenchuan PTSD data

fit <- easybgm_compare(list(group1, group2), 
                type = "binary", save = TRUE,
                iter = 50 # for demonstration only (> 5e4 recommended)
                )

summary(fit)

# For multigroup estimation
fit_multi <- easybgm_compare(data[1:200, 1:5], 
                group_indicator = rep(c(1, 2, 3, 4), each = 50),
                type = "binary", save = TRUE, 
                iter = 100 # for demonstration only (> 5e4 recommended)
                )

summary(fit_multi)

Edge evidence plot

Description

The edge evidence plot colors edges according to their hypothesis testing results: blue for included, light blue for weakly included, gray for inconclusive, light yellow for weakly excluded, and yellow for excluded. This plot can be used to visualize the hypothesis testing results whether edge presence or absence. The edge evidence plot can aid researchers in deciding which edges provide robust inferential conclusions

Usage

plot_edgeevidence(
  output,
  evidence_thresh = 10,
  split = FALSE,
  show = "all",
  ...
)

Arguments

Value

Returns a plot

Examples

library(easybgm)
library(bgms)

data <- na.omit(Wenchuan)
fit <- easybgm(data, type = "continuous",
                iter = 100  # for demonstration only (> 5e4 recommended)
                )

plot_edgeevidence(fit)

oldpar <- par(mfrow = c(1,1))

par(mfrow = c(1, 2))
plot_edgeevidence(fit, split = TRUE)

#' par(mfrow = c(1, 3))
plot_edgeevidence(fit, show = "included")
plot_edgeevidence(fit, show = "inconclusive")
plot_edgeevidence(fit, show = "excluded")

par(oldpar)

Network plot

Description

The network plot visualizes the strength of interactions between two nodes, the partial associations. Solely edges with a posterior inclusion probability larger than the exc_prob argument (default = 0.5) are shown. Edge thickness and saturation represent the strength of the association; the thicker the edge, the stronger the association. Red edges indicate negative relations and blue edges indicate positive associations.

Usage

plot_network(output, exc_prob = 0.5, evidence_thresh = 10, dashed = FALSE, ...)

Arguments

Value

Returns a plot

Examples

library(easybgm)
library(bgms)

data <- na.omit(Wenchuan)
fit <- easybgm(data, type = "continuous",
                iter = 100  # for demonstration only (> 5e4 recommended)
                )

plot_network(fit)

# Shows all edges with an inclusion probability larger than 0.1
plot_network(fit, exc_prob = 0.1)

# Indicate which edges have insufficient evidence for inclusion through a dashed line
plot_network(fit, dashed = TRUE, evidence_thresh = 10)

Print method for easybgm objects

Description

Used to print easybgm results. The nicest overview is created by first feeding it to summary()

Usage

## S3 method for class 'easybgm'
print(x, ...)

Arguments

Value

Prints the output of a Bayesian cross-sectional network model fitted with 'easybgm'

Print method for easybgm_compare objects

Description

Used to print easybgm results. The nicest overview is created by first feeding it to summary()

Usage

## S3 method for class 'easybgm_compare'
print(x, ...)

Arguments

Value

Prints the output of a Bayesian cross-sectional network comparison fitted with 'easybgm'

Plot sensitivity to edge inclusion prior setting

Description

For a given list of easybgm outputs with different prior edge inclusion probabilities, the function plots the percentage of edges that are included, excluded, and inconclusive.

Usage

plot_prior_sensitivity(output, ...)

Arguments

Details

Prior sensitivity plot

Value

Returns a plot

Examples

library(easybgm)
library(bgms)

#data <- na.omit(Wenchuan)
#fit1 <- easybgm(data, type = "ordinal",
#               iter = 100  # for demonstration only (> 5e4 recommended),
#                inclusion_probability = .1
#               )
#fit2 <- easybgm(data, type = "ordinal",
#                  iter = 100,
#                  inclusion_probability = .5
#             )
#fit3 <- easybgm(data, type = "ordinal",
#                iter = 100, inclusion_probability = .9)

#plot_prior_sensitivity(list(fit1, fit2, fit3))

Structure plot

Description

The plot shows the resulting graph structure, i.e. all edges with some evidence of inclusion (i.e., inclusion Bayes factor greater than 1).

Usage

plot_structure(output, ...)

Arguments

Value

Returns a plot

Examples

library(easybgm)
library(bgms)

data <- na.omit(Wenchuan)
fit <- easybgm(data, type = "ordinal",
                iter = 100  # for demonstration only (> 5e4 recommended)
               )

plot_structure(fit)

Plot Posterior Structure Probabilities

Description

Plots the posterior structure probabilities of all visited structures, sorted from the most to the least probable.

Usage

plot_structure_probabilities(output, as_BF = FALSE, ...)

Arguments

Value

Returns a plot

Examples

library(easybgm)
library(bgms)

data <- na.omit(Wenchuan)
fit <- easybgm(data, type = "ordinal", save = TRUE, edge_selection = TRUE,
                iter = 100  # for demonstration only (> 5e4 recommended)
                )

plot_structure_probabilities(fit)

Summary method for easybgm objects

Description

Used to create a object of easybgm results and in turn print it

Usage

## S3 method for class 'easybgm'
summary(object, evidence_thresh = 10, BF_uncertainty = FALSE, ...)

Arguments

Value

Creates and prints the output of a Bayesian cross-sectional network analysis. The summary output has four parts. The first part lists the package used, the number of variables, and the data type. The second part is a matrix of edge-specific information. Each edge is listed in a row. This row contains the posterior parameter estimate, the posterior inclusion probability, the inclusion Bayes factor, and the categorization of the edge. The category encodes whether an edge is included, excluded, or inconclusive based on the inclusion Bayes factor. Users can set the threshold for the Bayes factor classification with the evidence threshold. By default, the threshold is set to 10. The third part of the summary provides aggregated edge information. It lists the number of included, excluded, and inconclusive edges in the network, as well as the number of possible edges. This gives the user a quick overview of the robustness and density of the network. The higher the number of conclusive edges (i.e., classified as either included or excluded), the more robust the network. Conversely, if the network has a high percentage of inconclusive edges, the network is not robust. Researchers should refrain from making strong inferential conclusions. The final output section is a description of the structure uncertainty. It shows the number of structures visited, the number of possible structures, and the highest posterior structure probability. This last section can only be obtained for networks fitted with 'BDgraph' and 'bgms'.

Summary method for easybgm_compare objects

Description

Used to create a object of easybgm results and in turn print it

Usage

## S3 method for class 'easybgm_compare'
summary(object, evidence_thresh = 10, ...)

Arguments

Value

Creates and prints the output of a Bayesian cross-sectional network analysis. The summary output has four parts. The first part lists the package used, the number of variables, and the data type. The second part is a matrix of edge-specific information. Each edge is listed in a row. This row contains the posterior parameter estimate, the posterior inclusion probability, the inclusion Bayes factor, and the categorization of the edge. The category encodes whether an edge is included, excluded, or inconclusive based on the inclusion Bayes factor. Users can set the threshold for the Bayes factor classification with the evidence threshold. By default, the threshold is set to 10. The third part of the summary provides aggregated edge information. It lists the number of included, excluded, and inconclusive edges in the network, as well as the number of possible edges. This gives the user a quick overview of the robustness and density of the network. The higher the number of conclusive edges (i.e., classified as either included or excluded), the more robust the network. Conversely, if the network has a high percentage of inconclusive edges, the network is not robust. Researchers should refrain from making strong inferential conclusions. The final output section is a description of the structure uncertainty. It shows the number of structures visited, the number of possible structures, and the highest posterior structure probability. This last section can only be obtained for networks fitted with 'BDgraph' and 'bgms'.