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<p>In recent years, with the advent of open-source programming languages such as R and Python, there has been a resurgence of the DIY spirit in the experimental analysis of behavior and, more broadly, in experimental psychology. This paper introduces YEAB, an R package that streamlines the processing and analysis of data from numerous common behavioral paradigms. Key features include automated extraction of raw data from MED-PC output files, flexible modeling of interresponse times (including mixture-exponential approaches), single-trial analysis for fixed-interval and peak procedures, and model fitting of delay discounting data in intertemporal choice tasks. By consolidating core functions into one accessible package, YEAB empowers researchers to efficiently handle multi-level data structures, promote reproducible workflows, and expand the scope of behavioral experimentation. Collectively, these functionalities facilitate robust, transparent analyses, fostering open-science practices and supporting innovative exploration in EAB and related fields.</p>

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YEAB: YEAB Ease the analysis of behavior

  • 期刊论文

  • 发表日期：

    2025年6月16日

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  • Emmanuel Alcalá,
  • Pablo Saavedra,
  • Frida García-Rangel,
  • Rodrigo Sosa,
  • Víctor Reyes,
  • Jonathan Buriticá

摘要

In recent years, with the advent of open-source programming languages such as R and Python, there has been a resurgence of the DIY spirit in the experimental analysis of behavior and, more broadly, in experimental psychology. This paper introduces YEAB, an R package that streamlines the processing and analysis of data from numerous common behavioral paradigms. Key features include automated extraction of raw data from MED-PC output files, flexible modeling of interresponse times (including mixture-exponential approaches), single-trial analysis for fixed-interval and peak procedures, and model fitting of delay discounting data in intertemporal choice tasks. By consolidating core functions into one accessible package, YEAB empowers researchers to efficiently handle multi-level data structures, promote reproducible workflows, and expand the scope of behavioral experimentation. Collectively, these functionalities facilitate robust, transparent analyses, fostering open-science practices and supporting innovative exploration in EAB and related fields.