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Constructing and Modifying Sequence Statistics for relevent Using informR in ļæ½ļæ½.

Christopher Steven Marcum1, Carter T Butts2

  • 1National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, United States of America.

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|July 18, 2015
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Summary
This summary is machine-generated.

The informR package simplifies complex event history analysis in R. It provides user-friendly tools for building statistics for relational event models, making social action analysis more accessible.

Keywords:
relational eventsreleventsequence statistics

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Area of Science:

  • Social Network Analysis
  • Computational Social Science
  • Statistical Software Development

Background:

  • Analyzing complex event histories, particularly sequences of social actions (aā†’b), has been historically cumbersome.
  • Existing methods for relational event modeling often require complex data structures, posing a barrier to researchers.
  • The egocentric generalization of Butts' (2008) relational event framework necessitates user-friendly tools for sufficient statistic construction.

Purpose of the Study:

  • To introduce the informR package in R, designed to streamline the analysis of complex event histories.
  • To provide accessible tools for constructing sufficient statistics required by the relevent package's rem() model.
  • To demonstrate the utility of informR with real-world data from the American Time Use Survey.

Main Methods:

  • Development of the informR R package, offering user-friendly functions for data preparation.
  • Implementation of tools to simplify the creation of complex lists of arrays for rem() model fitting.
  • Application of informR to egocentric event data, accommodating multiple event types and support constraints.

Main Results:

  • The informR package successfully simplifies the construction of sufficient statistics for relational event models.
  • The package handles various complexities, including egocentric event data, multiple event types, and support constraints.
  • Illustrative examples using American Time Use Survey data showcase the practical application and ease of use.

Conclusions:

  • The informR package significantly lowers the barrier to entry for analyzing complex social action event sequences in R.
  • It provides a robust and user-friendly solution for researchers utilizing relational event modeling.
  • InformR enhances the accessibility and efficiency of social action analysis in computational social science.