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| 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 |