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rtmpt: An R package for fitting response-time extended multinomial processing tree models.

Raphael Hartmann1, Lea Johannsen2, Karl Christoph Klauer2

  • 1Department of Psychology, University of Freiburg, Engelbergerstrasse, 41, 79106, Freiburg, Germany. raphael.hartmann@psychologie.uni-freiburg.de.

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Summary
This summary is machine-generated.

This study introduces the R package rtmpt for easily fitting response-time extended multinomial processing tree (RT-MPT) models. It enables estimation of cognitive process completion times and offers features for flexible model specification and analysis.

Keywords:
Bayesian inferenceHierarchical modelingMultinomial modelingR packageRT-MPTResponse timertmpt

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Area of Science:

  • Cognitive psychology
  • Mathematical psychology
  • Computational statistics

Background:

  • Multinomial processing tree (MPT) models are established for cognitive process modeling.
  • Estimating cognitive process completion times requires advanced modeling techniques.
  • Response-time extended multinomial processing tree (RT-MPT) models offer a solution for time estimation.

Purpose of the Study:

  • To introduce the R package rtmpt for accessible fitting of RT-MPT models.
  • To provide a user-friendly tool for estimating cognitive process completion times.
  • To facilitate the application of RT-MPT models in cognitive research.

Main Methods:

  • Development of the open-source R package rtmpt.
  • Implementation of an altered C++ code for Markov Chain Monte Carlo (MCMC) sampling.
  • Validation of the hierarchical Bayesian algorithm using simulation-based calibration.

Main Results:

  • The rtmpt package allows easy fitting of RT-MPT models.
  • The package supports established MPT syntaxes and offers features like suppressing completion times and setting prior parameters.
  • Validation confirms the accuracy and reliability of the implemented algorithm.

Conclusions:

  • The rtmpt package provides a valuable and accessible tool for researchers interested in cognitive process timing.
  • The package simplifies the application of complex RT-MPT models.
  • Reproducibility of previous findings is demonstrated, enhancing confidence in the package's utility.