ggdmc: Bayeisan computation of response time models

Description Author(s) References


ggdmc uses the population-based Markov chain Monte Carlo to conduct Bayesian computation on cognitive models.


Yi-Shin Lin <>
Andrew Heathcote <>


Heathcote, A., Lin, Y.-S., Reynolds, A., Strickland, L., Gretton, M. & Matzke, D., (2018). Dynamic model of choice. Behavior Research Methods.

Turner, B. M., & Sederberg P. B. (2012). Approximate Bayesian computation with differential evolution, Journal of Mathematical Psychology, 56, 375–385.

Ter Braak (2006). A Markov Chain Monte Carlo version of the genetic algorithm Differential Evolution: easy Bayesian computing for real parameter spaces. Statistics and Computing, 16, 239-249.

ggdmc documentation built on May 2, 2019, 9:59 a.m.