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DMC with Parallel Computation and C++ Capabilities

ggdmc implements hierarchical Bayesian, evidence accumulation model (HB-EAM). This release includes drift-diffusion model (Ratcliff, 1978). Based on Voss, Rothermund, and Voss's (2004) fast-dm 30.2 density.c, the DDM in ggdmc implements the equations in their paper as C++ routines plus a new parallel integration function to handle high precision requirement.

ggdmc is derived from Andrew Heathcote's Dynamic Model of Choice (DMC), which has also implemented other numerous EAMs. Identical to DMC, ggdmc uses differential evolution Markov Chain Monte Carlo sampler to search optimal theta and phi that maximise posterior likelihood.

Getting Started

Here is a simple example extracted from Andrew Heathcote's DMC workshop materials. For further details, please see R help pages in this package.


## Use a 6-parameter drift-diffusion model  
## The only experimental factor is Stimulus type
m1 <- model.dmc(     = list(a="1", v="1", z="1", d="1", sz="1", sv="1", t0="1",st0="1"),
  constants = c(st0=0, d=0),          ## Fixed st0 and d at 0 = list(M = list(s1 = "r1", s2 = "r2")),
  factors   = list(S=c("s1", "s2")),  ## Two stimulus types  
  responses = c("r1","r2"),           ## Two response types
  type      = "rd")                   ## rd stands for Ratcliff's diffusion

## Set up a prior probability distribution setting
p.prior <- prior.p.dmc(
  dists = rep("tnorm", 6),
  p1    = c(a=2,  v=2.5, z=.5, sz=.3, sv=1,  t0=.3),
  p2    = c(a=.5, v=.5,  z=.1, sz=.1, sv=.3, t0=.05),
  lower = c(0,-5, 0, 0, 0, 0),
  upper = c(5, 7, 2, 2, 2, 2))

## Assume a true DDM parameter vector
p.vector <- c(a=1, v=1, z=.5, sz=.25, sv=0.2,t0=.15)

## Use simulate function to simulate choice-RT data 
## One usually would like to fit his/her own empirical data.
dat1 <- simulate(m1, nsim=1e2, p.vector=p.vector)

## Set up a data model instance. This binds the empirical/simulated data with 
## the model set-up
mdi1 <- data.model.dmc(dat1, m1)

## Initialise a small sample 
## (1) iteration number == 250 
## (2) thinning length == 1
## (3) prior distributions are listed in p.prior 
## (4) data == mdi1, an assumed model and a simulated/empirical data frame
samples0 <- samples.dmc(nmc=250, p.prior=p.prior, data=mdi1, thin=1)

## Fit the Bayesian model 
samples0 <- run.dmc(samples0)

## Check if model converged, etc.
## Potential scale reduction factors:
##    Point est. Upper C.I.
## a        1.16       1.29
## v        1.20       1.36
## z        1.18       1.33
## sz       1.12       1.21
## sv       1.24       1.48
## t0       1.16       1.34

plot(samples0)  ## Check traceplot to see if chains converged

## Further details and more arguments, please see

## For hierarchical Bayesian model 


  • R (>= 3.0.2)
  • Rcpp (>= 0.12.3), RcppArmadillo (>= 0.6.700.6.0), ggplot2 (>= 2.1.0), rtdists (>= 0.6-6), gridExtra (>= 2.2-1), ggmcmc (>= 0.7.3), ggthemes (>= 3.0.1), stats (>= 3.2.2), loo (>= 0.1.6), coda (>= 0.16-1)
  • Windows users need Rtools (>=, and Microsoft Visual Studio Community (>= 2015) (for Open MPI library and M_PI macro support)
  • OS X users need to install Open MPI library
  • Linux/Unix users may need to install Open MPI library, if it has not been installed.
  • Armadillo requires a recent compiler; for the g++ family at least version 4.6.* is required. Some Linux/Unix distributions may use a g++ earlier than 4.6.*.
  • This package uses Rcpp::Nullable, so Rcpp >= 0.12.1 is required.

Successful cases for Windows OS:

  • Microsoft Visual Studio Community 2015 (Version 14.0.25421.03 Update 3) on
    Windows 10 64 bits.
  • Microsoft Visual Studio Community 2015 (Version 14.0.24720.1 Update 1), with Rtools 3.4 on Windows 10 64 bits.

Unsuccseeful cases for Windows OS:

  • Microsoft Blend for Visual Studio Express 2015


From CRAN: install.packages("ggdmc")
From source: install.packages("ggdmc_0.1.3.5.tar.gz", repos = NULL, type="source")

### Other supporting packages for DMC (not necessary for ggdmc)
install.packages("coda_0.18-1.3.tar.gz", repos = NULL, type="source")
install.packages("tnorm_0.1.0.0.tar.gz", repos = NULL, type="source")


If you use this package, please cite the software, for example:

Lin, Y.-S., & Heathcote, A (in preparation). ggdmc: An R package for hierarchical Bayesian evidence accumulation models, using differential evolution Markov Chain Monte Carlo Sampler. Retrieved from


The ggdmc C++ codes are developed by Yi-Shin Lin.

The R codes mainly are incorporated from DMC developed by Andrew Heathcote.

If there is any bug been introduced inadvertently into DMC R codes, they are probably errors brought in by the first author. Please report any bugs you may find to the first author.


GPL-2. Please see for details.


  • density.cpp is based on Voss & Voss's (2012) density.c in fast-dm 30.2. Any identical parts in density.cpp belong to their copyright.
  • The tnorm_wrapper.cpp and tnorm.R are based on Jonathan Olmsted's RcppTN 0.1-8 ( and Christopher Jackson's R codes in msm. Any identical parts in tnorm_wrapper.cpp and tnrom.R belong to their copyright.
  • C++ codes in this package depend largely on Dirk Eddelbuettel, Romain Francois and Doug Bates's Rcpp, and RcppArmadillo.
  • Armadillo is a collection of C++ library for performing linear algebra, authored by Conrad Sanderson.
  • DEMCMC sampler is based on the following papers, Turner & Sederberg (2012), Turner et al (2013) and Ter Braak (2006).
  • Thanks to Matthew Gretton's consulation about rtdists's internal.


DMC with Parallel Computation and C++ Capabilities




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