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MPTinR: analysis of multinomial processing tree models in R.

Henrik Singmann1, David Kellen

  • 1Institut für Psychologie, Albert-Ludwigs-Universität Freiburg, Freiburg, Germany. henrik.singmann@psychologie.uni-freiburg.de

Behavior Research Methods
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Summary
This summary is machine-generated.

MPTinR is a new R software package for analyzing multinomial processing tree (MPT) models, offering enhanced flexibility and features for cognitive measurement and categorical data analysis.

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Area of Science:

  • Cognitive Science
  • Psychometrics
  • Computational Statistics

Background:

  • Multinomial processing tree (MPT) models are widely used for cognitive measurement with categorical data.
  • Existing software for MPT models can be limited in flexibility.
  • There is a need for advanced analytical tools within statistical programming environments.

Purpose of the Study:

  • To introduce MPTinR, a novel software package for R, to facilitate the analysis of MPT models.
  • To provide a more flexible modeling framework compared to standalone MPT software.
  • To incorporate advanced features for MPT model analysis and selection.

Main Methods:

  • Development of the MPTinR package in the R statistical programming language.
  • Implementation of functions for calculating Fisher information approximation for model complexity.
  • Inclusion of capabilities for fitting signal detection models and other non-MPT models.
  • Development of model selection functions for nested and non-nested candidate models.
  • Integration of multicore processing for efficient model fitting.

Main Results:

  • MPTinR is the first R package dedicated to MPT model analysis, offering a flexible framework.
  • The package includes novel features like Fisher information approximation for complexity and support for signal detection models.
  • MPTinR provides robust model selection capabilities across various candidate models.
  • Multicore fitting enhances computational efficiency for complex analyses.

Conclusions:

  • MPTinR significantly advances the analysis of MPT models by providing a flexible, feature-rich environment within R.
  • The package democratizes advanced MPT modeling and related analyses for researchers.
  • MPTinR is readily available, promoting wider adoption and application in cognitive and psychological research.