DDM                     The Diffusion Decision Model
DDMGNG                  The GNG (go/nogo) Diffusion Decision Model
LBA                     The Linear Ballistic Accumulator model
LNR                     The Log-Normal Race Model
RDM                     The Racing Diffusion Model
SDT                     Gaussian Signal Detection Theory Model for
                        Binary Responses
auto_thin.emc           Automatically Thin an emc Object
chain_n                 MCMC Chain Iterations
check.emc               Convergence Checks for an emc Object
compare                 Information Criteria and Marginal Likelihoods
compare_subject         Information Criteria For Each Participant
contr.anova             Anova Style Contrast Matrix
contr.bayes             Contrast Enforcing Equal Prior Variance on each
                        Level
contr.decreasing        Contrast Enforcing Decreasing Estimates
contr.increasing        Contrast Enforcing Increasing Estimates
credible.emc            Posterior Credible Interval Tests
credint.emc.prior       Posterior Quantiles
design                  Specify a Design and Model
ess_summary.emc         Effective Sample Size
fit.emc                 Model Estimation in EMC2
forstmann               Forstmann et al.'s Data
gd_summary.emc          Gelman-Rubin Statistic
get_BayesFactor         Bayes Factors
get_data.emc            Get Data
get_design.emc.prior    Get Design
get_pars                Filter/Manipulate Parameters from emc Object
get_prior.emc           Get Prior
hypothesis.emc          Within-Model Hypothesis Testing
init_chains             Initialize Chains
make_data               Simulate Data
make_emc                Make an emc Object
make_random_effects     Generate Subject-Level Parameters
mapped_pars             Parameter Mapping Back to the Design Factors
merge_chains            Merge Samples
model_averaging         Model Averaging
pairs_posterior         Plot Within-Chain Correlations
parameters.emc.prior    Return Data Frame of Parameters
plot.emc                Plot Function for emc Objects
plot.emc.design         Plot method for emc.design objects
plot.emc.prior          Plot a prior
plot_cdf                Plot Defective Cumulative Distribution
                        Functions
plot_density            Plot Defective Densities
plot_design.emc.design
                        Plot Design
plot_pars               Plots Density for Parameters
plot_relations          Plot Group-Level Relations
plot_sbc_ecdf           Plot the ECDF Difference in SBC Ranks
plot_sbc_hist           Plot the Histogram of the Observed Rank
                        Statistics of SBC
plot_stat               Plot Statistics on Data
predict.emc.prior       Generate Posterior/Prior Predictives
prior                   Specify Priors for the Chosen Model
prior_help              Prior Specification Information
profile_plot            Likelihood Profile Plots
recovery.emc            Recovery Plots
run_bridge_sampling     Estimating Marginal Likelihoods Using WARP-III
                        Bridge Sampling
run_emc                 Fine-Tuned Model Estimation
run_sbc                 Simulation-Based Calibration
sampled_pars            Get Model Parameters from a Design
samples_LNR             LNR Model of Forstmann Data (First 3 Subjects)
subset.emc              Shorten an emc Object
summary.emc             Summary Statistics for emc Objects
summary.emc.design      Summary method for emc.design objects
summary.emc.prior       Summary method for emc.prior objects
update2version          Update EMC Objects to the Current Version
