Package: imaginarycss 0.1.0

Sima Najafzadehkhoei

imaginarycss: Tools for Studying Imaginary Cognitive Social Structure

This package provides functions to measure and test imaginary cognitive social structure (CSS) motifs, which are patterns of perceived relationships among individuals in a social network. The package includes tools for calculating motif frequencies, comparing observed motifs to expected distributions, and visualizing motif structures. It implements the methods described in Tanaka and Vega Yon (2023) <doi:10.1016/j.socnet.2023.11.005>.

Authors:Sima Najafzadehkhoei [aut, cre], George Vega Yon [aut], Kyosuke Tanaka [aut]

imaginarycss_0.1.0.tar.gz
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imaginarycss_0.1.0.tgz(r-4.6-x86_64)imaginarycss_0.1.0.tgz(r-4.6-arm64)imaginarycss_0.1.0.tgz(r-4.5-x86_64)imaginarycss_0.1.0.tgz(r-4.5-arm64)
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imaginarycss_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
imaginarycss/json (API)

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

Bug tracker:https://github.com/gvegayon/imaginary-structures/issues

Pkgdown/docs site:https://gvegayon.github.io

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

cognitive-social-structurescsssnasocial-network-analysisquartocpp

3.78 score 1 stars 15 scripts 459 downloads 9 exports 2 dependencies

Last updated from:705000c8a5. Checks:10 OK, 3 WARNING. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK145
linux-devel-x86_64OK174
source / vignettesOK191
linux-release-arm64OK165
linux-release-x86_64OK144
macos-release-arm64OK165
macos-release-x86_64WARNING216
macos-oldrel-arm64WARNING228
macos-oldrel-x86_64WARNING261
windows-develOK122
windows-releaseOK132
windows-oldrelOK148
wasm-releaseOK148

Exports:barray_to_edgelistcount_imaginary_censuscount_recip_errorsnetsizenew_barry_graphnnetssample_css_networktest_imaginary_censustie_level_accuracy

Dependencies:barryRcpp

Network Perception Analysis
Imaginary Network Motifs: Testing Systematic Perception Errors | Setup | Data Overview | Creating the CSS Graphs | Observed Motif Counts | Null-Model Testing | Results | Full Summary | Tie-Level Accuracy | Conclusion
Rendered fromAdvanced-analysis.qmdusingquarto::html

Last update: 2026-02-24
Started: 2025-12-13

Real-world example of imaginarycss
Krackhardt Advice Network | Data and Perceptions | Individual Accuracy | Perceptual Structure | Organizational Takeaways
Rendered fromreal-world-example.qmdusingquarto::html

Last update: 2026-02-24
Started: 2025-12-13

Analyzing Perceptual Errors and Individual Accuracy
Perceptual Error Analysis | Setup: A Small Example Network | Reciprocity Errors | Imaginary Census | Individual Accuracy | Null Model Testing | Key Takeaways
Rendered fromerrors-and-individual-accuracy.qmdusingquarto::html

Last update: 2026-02-24
Started: 2025-12-13

Introduction to imaginarycss
Introduction | What Are Cognitive Social Structures? | Key Features | Creating Barry Graph Objects | From a List of Matrices | From a Block-Diagonal Matrix | Inspecting Graph Attributes
Rendered fromintro-basics.qmdusingquarto::html

Last update: 2026-02-24
Started: 2025-12-13

Readme and manuals

Help Manual

Help pageTopics
Retrieves the edgelist of a barry_graphbarray_to_edgelist
Computes census of imaginary errorscount_imaginary_census
Add a counter for reciprocity errorscount_recip_errors
Krackhardt High-Tech Managers Advice Networkkrackhardt_advice
Krackhardt Advice Network Perception Errorskrackhardt_advice_perceptions
Krackhardt High-Tech Managers Attributeskrackhardt_attributes
Krackhardt High-Tech Managers Friendship Networkkrackhardt_friendship
Krackhardt Friendship Network Perception Errorskrackhardt_friendship_perceptions
Krackhardt High-Tech Managers Reporting Networkkrackhardt_reports
Binary Array Graphbarry_graph netsize new_barry_graph new_barry_graph.list new_barry_graph.matrix nnets
Print Barry Graphprint.barry_graph
Summarize an imaginary censussummary.imaginary_census
Test imaginary census motifs against a null modelplot.imaginarycss_test print.imaginarycss_test summary.imaginarycss_test test_imaginary_census
Null distribution for Cognitive Imaginary Structuressample_css_network tie_level_accuracy