ggdmc: Bayeisan Computation for Cognitive Models

Description Author(s) References


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.


Yi-Shin Lin <[email protected]>
Andrew Heathcote <[email protected]>


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 Sept. 2, 2018, 1:03 a.m.