# Dynamic Model of Choice with Parallel Computation, and C++ Capabilities

## Documentation for package ‘ggdmc’ version 0.1.3.5

- DESCRIPTION file.
- Package NEWS.

## Help Pages

ggdmc-package | Supersonic DMC
acf.dmc | Plot an Autocorrelation Matrix
censor | Censor missing values and RT outliers
data.model.dmc | Bind Data and Models
ddmc | Compute Probability Density of Drift-Diffusion Model
ddmc_parallel | Compute Probability Density of Drift-Diffusion Model
density.dmc | Calculate Probability Density for an Experimental Condition
dprior | Calculate Prior Probability Density for an EAM
Dstats.ddm | Calculate Dstats of DDM Density
dtnorm | Truncated Normal Distribution
effectiveSize.dmc | Effective Sample Size for Estimating the Mean
fac2df | Convert factor levels to a data frame
gelman.diag.dmc | Gelman and Rubin Convergence Diagnostic
getAccumulatorMatrix | Map a parameter vector to an accumulator matrix
get_os | get_os Function
ggdmc | Supersonic DMC
g_minus | Calculate Drift-diffusion Probability Density
g_minus_parallel | Calculate Drift-diffusion Probability Density
g_plus | Calculate Drift-diffusion Probability Density
g_plus_parallel | Calculate Drift-diffusion Probability Density
h.run.dmc | Fit an EAM with Multiple Participants
h.samples.dmc | Set up a DMC Sample with Multiple Participants
h.simulate.dmc | Simulate Choice-RT Data for Multiple Participants
initialise_data | Set up a DMC Sample for a Participant
initialise_hyper | Set up a DMC Sample for Multiple Participants
likelihood | Calculate Log-Likelihood
likelihood.default | Calculate Log-Likelihood
likelihood.norm | Calculate Log-Likelihood
likelihood.rd | Calculate Log-Likelihood
mcmc.list.dmc | Create a mcmc.list in DMC format
model.dmc | Creating a Model Object
p.df.dmc | Gets Parameter Data Frame
pairs.dmc | Create a Plot Matrix of Posterior Simulations
phi.as.mcmc.list | Convert Phi to a Theta Vector
pick.stuck.dmc | Find Stuck Chains
plot.dmc | Plot DMC Samples
plot.dmc.list | Plot a DMC Sample with Multiple Participants
plot.hyper | Plot DMC Samples at the Hyper level
plot.pp.ggdmc | Posterior Predictive Plot
plot_cell_density | Plot Distributions for Each Cell
plot_dist | Plot Cell Density
plot_prior | Plot Prior Probability Density
plot_priors | Plot Prior Probability Density
post.predict.ggdmc | Simulate Post-predictive Sample
print_cell_p | Print accumulator x internal parameter type matrix for each cell
prior.p.dmc | Makes a list of prior distribution parameters.
profile.dmc | Profile a DMC Object
rprior | Generate Random Numbers from Prior Probability Distribution
rtnorm | Truncated Normal Distribution
run.dmc | run function
run_data | Run a Bayesian EAM Model for Fixed-effect or Random-effect
run_data_parallel | Run a Bayesian EAM Model for Fixed-effect or Random-effect
run_hyper | Run a Bayesian EAM Model for Fixed-effect or Random-effect
run_hyper_parallel | Run a Bayesian EAM Model for Fixed-effect or Random-effect
samples.dmc | Initialising a DMC samples
simulate.dmc | Simulate Responses from an EAM
summary.dmc | Summarise a DMC Sample with One Participant
summary.dmc.list | Summarise a DMC Sample with Multiple Participants
summary.hyper | Summarise a DMC Sample with Multiple Participant at the Hyper-level
summed_log_likelihood | Sum and Log Probability Density of a EAM model
summed_log_likelihood_parallel | Sum and Log Probability Density of a EAM model
summed_log_prior | Sum and Log Prior Density of a EAM model
theta.as.mcmc.list | Convert Theta to a mcmc List
transform | Transform Parameter Data Frame
transform.norm | Transform Parameter Data Frame
transform.rd | Transform Parameter Data Frame
view | Inspect Prior Distribution Settings