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mixtur is an R package for designing, analysing, and modellingcontinuous report visual short-term memory studies. The package allowsusers to implement the 2-component (Zhang & Luck, 2008) and 3-component(Bays, Catalao, & Husain, 2009) mixture models of continuous-reportvisual short-term memory data. The package can also fit & simulate theslots and slots-plus averaging models of Zhang & Luck.

The package allows users to:

• Obtain summary statistics of response error and response precision ofbehavioural data
• Produce publication-ready plots of behavioural data
• Fit both the 2- and 3-component models to user data
• Plot the goodness of model fit to user data
• Simulate artificial data from both models
• Conduct formal model competition analysis

You can install the released version of mixtur (v1.2.2) fromCRAN with:

install.packages("mixtur")

The development version can be installed fromGitHub with:

# install.packages("devtools")devtools::install_github("JimGrange/mixtur")

We have an academic publication showing users how to use the package.Here we provide the link to the final publication, as well as a link tothe pre-print of the paper:

• Grange, J.A. & Moore, S.B. (2022). mixtur: An R package for designing,analysing, and modelling continuous report visual short-term memorystudies.Behavior Research Methods, 54, 2071–2100.
  • FinalPublication
  • Preprint

The paper also includes several simulation studies exploring someproperties of the models (including parameter recovery simulations,model recovery simulations) and provides concrete recommendations toresearchers wishing to use mixture modelling in their own research.

• We are grateful to Ed. D.J. Berry who contributed to the packagedevelopment.

• Portions of the package code have been adapted from code written byPaul Bays in Matlab, with permission. We are extremely grateful toPaul Bays for this permission. Seehttps://paulbays.com.

• Bays, P. M., Catalao, R. F. G., & Husain, M. (2009). The precision ofvisual working memory is set by allocation of a shared resource.Journal of Vision, 9(10): 7, 1–11.

• Zhang, W., & Luck, S. J. (2008). Discrete fixed-resolutionrepresentations in visual working memory.Nature, 453, 233–235.