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PreprintCC BY 4.0PsyArXiv

Introducing ResIN: An Applied Tutorial to the Response-Item-Networks Package for R

  • Philip Warncke
  • Dino Carpentras
  • Adrian Lueders
  • Michael Quayle
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Abstract

We introduce ResIN, a package for the R programming language designed to estimate, visualize, and analyze Response-Item Networks (ResIN ). ResIN is an increasingly popular method for modeling complex socio-political attitude systems as sparse, spatially interpretable networks. The package simplifies and unifies the underlying workflow while offering a range of convenience features, including network plotting, community detection, psychometric score extraction, bootstrap-based uncertainty estimation, and the export of ResIN objects to other statistical software environments. We provide an overview of the ResIN method, its computational implementation in R, and demonstrate the utility of key package features through an applied replication tutorial based on a recent study by Lüders et al., 2024

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Cite

BibTeXRIS
BibTeX
@misc{warncke2026introducing,
  title = {{Introducing ResIN: An Applied Tutorial to the Response-Item-Networks Package for R}},
  author = {Philip Warncke and Dino Carpentras and Adrian Lueders and Michael Quayle},
  howpublished = {PsyArXiv},
  year = {2026},
  month = oct,
  doi = {10.31234/osf.io/7jvqc_v2},
  url = {https://osf.io/preprints/psyarxiv/7jvqc_v2/},
  note = {CC BY 4.0},
}
RIS
TY  - UNPB
TI  - Introducing ResIN: An Applied Tutorial to the Response-Item-Networks Package for R
AU  - Philip Warncke
AU  - Dino Carpentras
AU  - Adrian Lueders
AU  - Michael Quayle
PY  - 2026
DA  - 2026/10/30
PB  - PsyArXiv
DO  - 10.31234/osf.io/7jvqc_v2
UR  - https://osf.io/preprints/psyarxiv/7jvqc_v2/
AB  - We introduce ResIN, a package for the R programming language designed to estimate, visualize, and analyze Response-Item Networks (ResIN ). ResIN is an increasingly popular method for modeling complex socio-political attitude systems as sparse, spatially interpretable networks. The package simplifies and unifies the underlying workflow while offering a range of convenience features, including network plotting, community detection, psychometric score extraction, bootstrap-based uncertainty estimation, and the export of ResIN objects to other statistical software environments. We provide an overview of the ResIN method, its computational implementation in R, and demonstrate the utility of key package features through an applied replication tutorial based on a recent study by Lüders et al., 2024
ER  -