ggdmc evolves from Dynamic Models of Choice (DMC), using graphic styles of ggplot2, highly efficient computations of Armadillo C++ and Rcpp and connecting GPU parallel computation to ppda to make fitting complex cognitive models feasible. ggdmc uses the sampling technique of population-based Monte Chain Monte Carlo.
Heathcote, A., Lin, Y.-S., Reynolds, A., Strickland, L., Gretton, M. &
Matzke, D., (2018). Dynamic model of choice.
Behavior Research Methods.
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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.
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