Package: InterconectaR 1.0.1

InterconectaR: Psychological Network Analysis and Psychometric Insights

Estimates psychological networks by group for research in the social sciences and complements them with psychometric modeling. It summarises centrality and bridge centrality indices, evaluates the stability of edges and centrality from bootstrap results, refines items by the stability of exploratory graph analysis (EGA), and plots networks with the variance explained of each node. It also estimates an a priori sample size for EGA through Monte Carlo simulation and includes an interactive application to plan the sample size of a network study. Methods follow Epskamp, Borsboom and Fried (2018) <doi:10.3758/s13428-017-0862-1> and Golino and Epskamp (2017) <doi:10.1371/journal.pone.0174035>.

Authors:José Ventura-León [aut, cre]

InterconectaR_1.0.1.tar.gz
InterconectaR_1.0.1.zip(r-4.7-any)InterconectaR_1.0.1.zip(r-4.6-any)InterconectaR_1.0.1.zip(r-4.5-any)
InterconectaR_1.0.1.tgz(r-4.6-any)InterconectaR_1.0.1.tgz(r-4.5-any)
InterconectaR_1.0.1.tar.gz(r-4.7-any)InterconectaR_1.0.1.tar.gz(r-4.6-any)
InterconectaR_1.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
InterconectaR/json (API)

# Install 'InterconectaR' in R:
install.packages('InterconectaR', repos = c('https://jventural.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/jventural/interconectar/issues

On CRAN:

Conda:

3.60 score 48 exports 210 dependencies

Last updated from:6d691d0eb7. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK284
source / vignettesOK276
linux-releaseOK239
macos-releaseOK199
macos-oldrelOK187
windows-develOK165
windows-releaseOK156
windows-oldrelOK185
wasm-releaseOK190

Exports:boot_and_evaluatecalculate_centrality_sd_correlationcentrality_bridge_plotcentrality_bullet_plotcentrality_cleveland_plotcentrality_duo_plotcentrality_plots2choose_best_methodcombine_graphs_centralitycombine_graphs_centrality2combine_groupBycompute_netScoresconvert_EGA_list_to_dfconvert_EGA_to_dfDensity_reportestimate_networks_by_groupfilter_correlation_stabilityget_edge_weights_summarymake_likert_thresholdsmgm_error_metricsmgm_errors_groupsnet_reduce2optimize_leiden_resolutionplot_avg_TEFIplot_centrality_by_groupplot_centrality_stabilityplot_failure_decompositionplot_latent_network_diagramplot_lvmplot_netplot_net_groupplot_networks_by_groupplot_performance_metricsplot_power_curve_ciplot_sample_size_EGA_montecarloprocess_LCTqgraph_centrality_panelrefine_items_by_stabilityrun_EGA_combinationsrun_netpowerlabsample_size_EGA_controlsample_size_EGA_montecarloshow_plotsshow_recommendationstructure_groupssummarise_case_drop_stabilitysummarise_nonparametric_edgessummary_metrics

Dependencies:abindbackportsbase64encbayestestRbitbit64bootbootnetbroombslibcachemCairocarcarDatacheckmateclassclicliprclueclustercocorcodacodetoolscolorspacecorpcorcorrelationcorrplotcowplotcpp11crayoncurldata.tabledatawizarddendextendDerivdigestdoBydoParalleldplyre1071EGAneteigenmodelellipseevaluatefarverfastmapfdrtoolfontawesomeforcatsforeachforecastforeignFormulafracdifffsfuturefuture.applygdatagenericsGGallyggforceggplot2ggplotifyggpubrggrepelggsciggsignifggstatsglassoglassoFastglmnetglobalsglueGPArotationgridExtragridGraphicsgtablegtoolshavenhighrHmischmshtmlTablehtmltoolshtmlwidgetsigraphinsightIsingFitIsingSamplerisobanditeratorsjomojpegjquerylibjsonliteknitrlabelinglatticelavaanlifecyclelistenvlme4lmtestmagickmagrittrmantarMASSmathjaxrMatrixMatrixModelsmemoisemgcvmgmmicemimeminqamitmlmnormtmodelrmvtnormnetworkNetworkToolboxnetworktoolsnlmenloptrnnetnnlsnumDerivordinalpanparallellyparameterspatchworkpbapplypbivnormpbkrtestpillarpkgconfigplotrixplyrpngpolyclippolynomppcorprettyunitsprogressprogressrproxypsychpurrrpwrqgraphquadprogquantregR.matlabR.methodsS3R.ooR.utilsR6rappdirsrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreadrreformulasreshape2rlangrmarkdownrpartrstatixrstudioapiS7sassscalessemPlotshapesmacofsnasnowSparseMstatnet.commonstringistringrsurvivalsystemfontstibbletidyrtidyselecttimeDatetinytextweenrtzdbucminfurcautf8vctrsviridisviridisLitevroomweightswesandersonwithrwordcloudxfunyamlyulab.utilszoo

Readme and manuals

Help Manual

Help pageTopics
Bootstrap and Evaluate Network Analysisboot_and_evaluate
Centrality-SD Correlationcalculate_centrality_sd_correlation
Bridge Centrality Plotcentrality_bridge_plot
Bullet Chart para Indices de Centralidadcentrality_bullet_plot
Cleveland Dot Plot para Indices de Centralidadcentrality_cleveland_plot
Dual Centrality Plotcentrality_duo_plot
Centrality Plot (Version 2)centrality_plots2
Choose Best Methodchoose_best_method
Combine Network and Centrality Graphscombine_graphs_centrality
Combine Network and Centrality Graphs (Version 2)combine_graphs_centrality2
Combine Group Plotscombine_groupBy
Compute Network Scorescompute_netScores
Convert EGA List to Data Frameconvert_EGA_list_to_df
Convert EGA Result to Data Frameconvert_EGA_to_df
Network Density ReportDensity_report
Estimate Networks by Groupestimate_networks_by_group
Filter Correlation Stabilityfilter_correlation_stability
Edge Weights Summaryget_edge_weights_summary
Generate Likert-Style Threshold Patternsmake_likert_thresholds
MGM Error Metricsmgm_error_metrics
MGM Errors by Groupmgm_errors_groups
Network Reductionnet_reduce2
Optimize Leiden Resolutionoptimize_leiden_resolution
Plot Average TEFI by Sample Sizeplot_avg_TEFI
Plot centrality measures by groupplot_centrality_by_group
Plot centrality stability diagnosticsplot_centrality_stability
Plot Replication-Level Failure Decomposition Per Sample Sizeplot_failure_decomposition
Plot a latent network diagram (SEM path diagram)plot_latent_network_diagram
Plot Latent Variable Modelsplot_lvm
Plot a single network with R2 progress ringsplot_net
Plot two networks side by side with R2 progress ringsplot_net_group
Plot networks by group as a combined panelplot_networks_by_group
Plot Network Performance Metricsplot_performance_metrics
Plot the Power Curve with Bootstrap Confidence Bandplot_power_curve_ci
Plot Monte Carlo EGA Sample Size Resultsplot_sample_size_EGA_montecarlo
Narrative summary of a sample-size simulationprint.sample_size_EGA_montecarlo
Print method for scale_network_analysis objectsprint.scale_network_analysis
Process LCT (Loadings Comparison Test) resultsprocess_LCT
Create a combined panel with qgraph network and centrality plotqgraph_centrality_panel
Iteratively refine items by bootstrap stabilityrefine_items_by_stability
Run EGA Combinationsrun_EGA_combinations
Launch the NetPowerLab Shiny Applicationrun_netpowerlab
Default control parameters for 'sample_size_EGA_montecarlo()'sample_size_EGA_control
A Priori Sample Size Estimation for EGA Using Monte Carlo Simulationsample_size_EGA_montecarlo
Visualize all plots stored in a 'sample_size_EGA_montecarlo' objectshow_plots
One-line dynamic recommendation messageshow_recommendation
Structure Groupsstructure_groups
Summarise Case Drop Stabilitysummarise_case_drop_stability
Summarise Nonparametric Edgessummarise_nonparametric_edges
Summarize Network Performance Metricssummary_metrics