Package index • EMC2

Models

DDM(): The Diffusion Decision Model

LBA(): The Linear Ballistic Accumulator model

LNR(): The Log-Normal Race Model

RDM(): The Racing Diffusion Model

Design and estimation

fit(): Model estimation in EMC2

design(): Specify a design and model

make_emc(): Make an emc object

init_chains(): Initialize chains

run_emc(): Custom function for more controlled model estimation

mapped_par(): Parameter mapping back to the design factors

sampled_p_vector(): Get model parameters from a design

profile_plot(): Likelihood profile plots

plot_defective_density(): Plot defective densities for each subject and cell

Model checking

check(): Convergence checks for an emc object

summary(<emc>): Summary statistics for emc objects

ess_summary(): Effective sample size

gd_summary(): Gelman-Rubin statistic

chain_n(): chain_n()

plot(<emc>): Plot function for emc objects

pairs_posterior(): Plot within-chain correlations

Posterior inference

posterior_summary(): Posterior quantiles

parameters(): Returns a parameter type from an emc object as a data frame.

get_pars(): Filter/manipulate parameters from emc object

plot_pars(): Plots density for parameters

plot_relations(): Plot relations

merge_chains(): Merge samples

subset(<emc>): Shorten an emc object

Posterior predictives and recovery

predict(<emc>): Generate posterior predictives

plot_fit(): Posterior predictive checks

make_data(): Simulate data

make_random_effects(): Make random effects

recovery(): Recovery plots

get_data(): Get data

Model comparison

compare(): Information criteria and marginal likelihoods

compare_subject(): Information criteria for each participant

run_bridge_sampling(): Estimating Marginal likelihoods using WARP-III bridge sampling

get_BayesFactor(): Bayes Factors

credible(): Posterior credible interval tests

hypothesis(): Within-model hypothesis testing

Priors

plot_prior(): Title

prior(): Prior specification

Contrasts

contr.anova(): Anova style contrast matrix

contr.bayes(): Contrast to enforce equal prior variance on each level

contr.decreasing(): Contrast to enforce decreasing estimates

contr.increasing(): Contrast to enforce increasing estimates

Simulation-based calibration

plot_sbc_ecdf(): Plot the ECDF difference in SBC ranks

plot_sbc_hist(): Plot the histogram of the observed rank statistics of SBC

run_sbc(): Simulation-based calibration

Included data

forstmann: Forstmann et al.'s data

samples_LNR: An emc object of an LNR model of the Forstmann dataset using the first three subjects