Publications
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Gronau, Q. F. & Singmann, H. (2018). bridgesampling: Bridge sampling for marginal likelihoods
and Bayes Factors. R package version 0.7-2.Ly, A., Boehm, U., Heathcote, A., Turner, B.M., Forstmann, B., Marsman, M., & Matzke, D. (2018). A flexible and efficient hierarchical Bayesian approach to the exploration of individual differences in cognitive-model-based neuroscience. In A.A. Moustafa (Ed.), Computational models of brain and behavior (pp. 467-480). Wiley Blackwell.
Matzke, D., Verbruggen, F., & Logan, G. (2018). The stop-signal paradigm. In E.-J. Wagenmakers & J.T. Wixted (Eds.), Stevens’ handbook of experimental psychology and cognitive neuroscience, Volume five: Methodology (4th ed., pp. 383-427). John Wiley & Sons, Inc.
Nunez, M.D., Horton, C., Deng, s., Winter, W., & Srinivasan, R. (2018) – artscreenEEG: MATLAB repository to perform basic artifact correction on EEG data. MATLAB package version 0.14.2
Wagenmakers, E.-J., Verhagen, A.J., Ly, A., Matzke, D., Steingroever, H., Rouder, J.N., & Morey, R.D. (2017). The need for Bayesian hypothesis testing in psychological science. In S.O. Lilienfeld & I. Waldman (Eds.), Psychological science under scrutiny: Recent challenges and proposed solutions (pp. 123-138). John Wiley and Sons.
Donkin, C., Rae, B., Heathcote, A. & Brown, S.D. (2015). Why is accurately labelling simple magnitudes so hard? In J. R. Busemeyer, J. T. Townsend, Z. J. Wang, & A. Eidels, (Eds.), Oxford handbook of computational and mathematical psychology (pp. 121-141). Oxford University Press.
Heathcote, A., Brown, S.D. & Wagenmakers, E.-J. (2015). An introduction to good practices in cognitive modeling. In B. U. Forstmann, & E.-J. Wagenmakers (Eds.), An introduction to model-based cognitive neuroscience (pp. 25-48). Springer: New York.
Vandekerckhove, J., Matzke, D., & Wagenmakers, E.-J. (2015). Model comparison and the principle of parsimony. In J. R. Busemeyer, J. T. Townsend, Z. J. Wang, & A. Eidels (Eds.), The Oxford handbook of computational and mathematical psychology (pp. 300-319). Oxford University Press. [code for computing MDL and Bayes factors]