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Modelling animal network data in R using STRAND.

Cody T Ross1, Richard McElreath1, Daniel Redhead1

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Summary
This summary is machine-generated.

This study introduces STRAND, an R package simplifying generative network models for animal social network analysis. It enables researchers to easily apply advanced Bayesian models to diverse social data.

Keywords:
R softwareanimal networksgenerative modelssocial networkssocial relations

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Area of Science:

  • Ecology
  • Network Science
  • Computational Biology

Background:

  • Generative modeling is increasingly called for in social network analysis.
  • Implementing these models is challenging for end-users like field researchers due to limited software.
  • Existing methods are often less accessible than permutation-based approaches.

Purpose of the Study:

  • To introduce the STRAND R package for accessible generative network modeling.
  • To provide a user-friendly tool for Bayesian analysis of animal social network data.
  • To demonstrate the application of generative models to various network data types.

Main Methods:

  • Development of the STRAND R package.
  • Implementation of generative models including stochastic block models and social relation models.
  • Use of simple, base R syntax for ease of application.
  • Provision of a tutorial for modeling proportion, count, or binary network data.

Main Results:

  • STRAND offers a suite of generative models for Bayesian analysis of animal social network data.
  • The package supports diverse data types (proportion, count, binary) and modeling frameworks.
  • STRAND simplifies the application of complex network models for typical end-users.

Conclusions:

  • STRAND significantly lowers the barrier to entry for applying generative network models in animal behavior research.
  • The package facilitates broader use of advanced analytical techniques in social network analysis.
  • STRAND enhances the accessibility of sophisticated modeling for field researchers and ecologists.