README

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InterconectaR

Psychological Network Analysis and Psychometric Insights
A comprehensive R package for constructing, analyzing, and visualizing psychological networks in social science research.

Author’s website

CRAN version License

Overview

InterconectaR provides an integrated toolkit for network analysis and psychometric modeling. It includes 49 functions organized around six core areas:

Network Estimation – Estimate and compare networks across groups using multiple methods
Centrality & Bridge Analysis – Calculate, compare, and visualize centrality and bridge metrics
Advanced Visualization – Publication-ready plots for networks, centrality indices, and SEM diagrams
Community Detection (EGA) – Exploratory Graph Analysis workflows with stability refinement
Model Evaluation & Stability – Bootstrap diagnostics, case-dropping stability, and performance metrics
Sample Size Planning – Monte Carlo sample size for EGA and an interactive application for network designs

Installation

Install the latest version from GitHub:

# install.packages("remotes")
remotes::install_github("jventural/InterconectaR")

Main Functions

Network Estimation

Function	Description
`estimate_networks_by_group()`	Estimate network models separately for each group in the data
`run_EGA_combinations()`	Run Exploratory Graph Analysis across multiple parameter combinations
`optimize_leiden_resolution()`	Optimize the resolution parameter for Leiden community detection

Centrality & Bridge Analysis

Function	Description
`calculate_centrality_sd_correlation()`	Correlate centrality indices with variable standard deviations
`centrality_bridge_plot()`	Comparative bridge centrality plot for two groups
`centrality_bullet_plot()`	Bullet chart for centrality indices
`centrality_cleveland_plot()`	Cleveland dot plot for centrality metrics
`centrality_duo_plot()`	Side-by-side comparison of two centrality measures
`centrality_plots2()`	Centrality plot with optional bridge metrics
`plot_centrality_by_group()`	Plot centrality measures by group

Visualization

Function	Description
`plot_net()`	Network plot with R2 progress rings
`plot_net_group()`	Two networks side by side with R2 progress rings
`plot_networks_by_group()`	Combined network panel by group
`plot_latent_network_diagram()`	SEM path diagram for latent network models
`plot_lvm()`	Latent variable model visualization
`combine_graphs_centrality()`	Combine qgraph network with centrality line plot
`combine_graphs_centrality2()`	Combine network and centrality graphs (version 2)
`combine_groupBy()`	Combine network, centrality, and bridge plots into labeled panel
`qgraph_centrality_panel()`	Combined panel with qgraph network and centrality plot

Community Detection (EGA)

Function	Description
`compute_netScores()`	Compute network scores with stability analysis using EGA
`convert_EGA_to_df()`	Extract network metrics from a single EGA result
`convert_EGA_list_to_df()`	Convert a list of EGA results into a combined data frame
`refine_items_by_stability()`	Iteratively refine items by bootstrap stability

Model Evaluation & Stability

Function	Description
`boot_and_evaluate()`	Bootstrap and evaluate network analysis
`filter_correlation_stability()`	Extract and filter correlation stability indices
`plot_centrality_stability()`	Plot centrality stability diagnostics
`summarise_case_drop_stability()`	Summarise case-dropping bootstrap stability
`summarise_nonparametric_edges()`	Summarise nonparametric edge bootstrap results
`summary_metrics()`	Summarize network performance metrics
`plot_avg_TEFI()`	Plot average TEFI by sample size
`plot_performance_metrics()`	Plot network performance metrics across conditions
`process_LCT()`	Process Loadings Comparison Test results

Utilities

Function	Description
`Density_report()`	Calculate and report network density statistics
`get_edge_weights_summary()`	Descriptive statistics for network edge weights
`mgm_error_metrics()`	Fit a Mixed Graphical Model and extract prediction errors
`mgm_errors_groups()`	MGM error metrics by group
`net_reduce2()`	Reduce redundant node pairs using PCA or goldbricker selection
`choose_best_method()`	Select the best estimation method based on correlation analysis
`structure_groups()`	Structure group labels for network communities

Sample Size Planning for EGA

Function	Description
`sample_size_EGA_montecarlo()`	A priori sample size for EGA through Monte Carlo simulation
`sample_size_EGA_control()`	Advanced settings of the simulation (targets, thresholds, algorithm)
`make_likert_thresholds()`	Likert thresholds with floor or ceiling effects for the simulated items
`plot_sample_size_EGA_montecarlo()`	Recovery metrics across the candidate sample sizes
`plot_power_curve_ci()`	Power curve with its bootstrap band and the recommended n
`plot_failure_decomposition()`	Which constraint fails at each sample size
`show_recommendation()`, `show_plots()`	One-line recommendation and the stored plots

Interactive Application

Function	Description
`run_netpowerlab()`	Launch NetPowerLab: plan the sample size of a network study with `bootnet::netSimulator()` and `powerly::powerly()`

InterconectaR::run_netpowerlab()

SemPowerLab, the companion application for designs with latent variables, is part of the PsyMetricTools package (run_sempowerlab()).

The reference network is declared block by block, one block per instrument, and acts as the effect size of the design. The app reports what is recovered at each sample size (sensitivity, specificity and the correlation between estimated and true weights), recommends the sample size that reaches a declared performance, and writes both the reproducible script and a draft of the Participants section. It needs shiny, bslib and powerly, which are suggested rather than required: install.packages(c("shiny", "bslib", "powerly")).

Examples

The examples use simulated data: eight items that measure two correlated factors, answered by two groups.

library(InterconectaR)

set.seed(123)
n <- 300
f1 <- rnorm(n)
f2 <- 0.3 * f1 + rnorm(n)
items <- data.frame(
  sapply(1:4, function(i) 0.7 * f1 + rnorm(n, 0, 0.7)),
  sapply(1:4, function(i) 0.7 * f2 + rnorm(n, 0, 0.7))
)
names(items) <- c(paste0("A", 1:4), paste0("B", 1:4))
items$sex <- rep(c("Female", "Male"), each = n / 2)

Estimate and visualize networks by group

# Estimate networks for each group
nets <- estimate_networks_by_group(
  data = items,
  group_var = "sex",
  columns = names(items)[1:8],
  default = "EBICglasso"
)

# Network of one group with its centrality plot
net <- nets$Female
g <- qgraph::qgraph(net$graph, DoNotPlot = TRUE)
cent <- centrality_plots2(g, net,
                          groups = rep(c("A", "B"), each = 4),
                          measure1 = "Bridge Expected Influence (1-step)")
r2 <- mgm_error_metrics(items[items$sex == "Female", 1:8],
                        type = rep("g", 8), level = rep(1, 8))$R2

combine_graphs_centrality(
  Figura1_Derecha = cent$plot,
  network = net,
  groups = list(A = 1:4, B = 5:8),
  error_Model = r2
)

Centrality comparison across groups

# Bridge centrality comparison
centrality_bridge_plot(
  networks_groups = nets,
  group_names = c("Female", "Male"),
  measure = "Bridge Expected Influence (1-step)",
  communities = rep(c("A", "B"), each = 4)
)

Compute network scores with EGA

results <- compute_netScores(
  item_prefixes = c("A", "B"),
  data = items,
  stability_threshold = 0.70,
  stability_iter = 500,
  rename_dims = FALSE
)

A priori sample size for EGA

res <- sample_size_EGA_montecarlo(
  community_sizes = c(4, 4),
  within_edge = 0.20,
  sample_sizes = c(150, 250, 400, 600),
  n_rep = 500,
  seed = 2026
)
res
plot_power_curve_ci(res)

Citation

Ventura-Leon, J. (2026). InterconectaR: Psychological Network Analysis and Psychometric Insights [R package]. GitHub. https://github.com/jventural/InterconectaR

Author

Jose Ventura-Leon - ORCID: 0000-0003-2996-4244 - Email: jventuraleon@gmail.com - Web: joseventuraleon.com

License

GPL (>= 3)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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