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

DDMGNG(): The GNG (go/nogo) Diffusion Decision Model

SDT(): Gaussian Signal Detection Theory Model for Binary Responses

Design and Mapping

design(): Specify a Design and Model

mapped_pars(): Parameter Mapping Back to the Design Factors

sampled_pars(): Get Model Parameters from a Design

plot_design(): Plot Design

plot(<emc.design>): Plot method for emc.design objects

summary(<emc.design>): Summary method for emc.design objects

get_design(): Get Design

group_design(): Create Group-Level Design Matrices

Prior Specification

prior(): Specify Priors for the Chosen Model

summary(<emc.prior>): Summary method for emc.prior objects

plot(<emc.prior>): Plot a prior

predict(<emc.prior>) predict(<emc>): Generate Posterior/Prior Predictives

get_prior(): Get Prior

prior_help(): Prior Specification Information

credint(): Posterior Quantiles

Estimation

init_chains(): Initialize Chains

make_emc(): Make an emc Object

fit(): Model Estimation in EMC2

run_emc(): Fine-Tuned Model Estimation

Model checking

check(): Convergence Checks for an emc Object

summary(<emc>): Summary Statistics for emc Objects

credint(): Posterior Quantiles

ess_summary(): Effective Sample Size

gd_summary(): Gelman-Rubin Statistic

chain_n(): MCMC Chain Iterations

plot(<emc>): Plot Function for emc Objects

pairs_posterior(): Plot Within-Chain Correlations

Posterior Inference

credint(): Posterior Quantiles

parameters(): Return Data Frame of Parameters

get_pars(): Filter/Manipulate Parameters from emc Object

plot_pars(): Plots Density for Parameters

plot_relations(): Plot Group-Level Relations

merge_chains(): Merge Samples

subset(<emc>): Shorten an emc Object

auto_thin(): Automatically Thin an emc Object

Data Generation and Recovery

predict(<emc.prior>) predict(<emc>): Generate Posterior/Prior Predictives

recovery(): Recovery Plots

make_data(): Simulate Data

make_random_effects(): Generate Subject-Level Parameters

get_data(): Get Data

profile_plot(): Likelihood Profile Plots

Model Fit

plot_density(): Plot Defective Densities

plot_cdf(): Plot Defective Cumulative Distribution Functions

plot_stat(): Plot Statistics on Data

Model Comparison

compare(): Information Criteria and Marginal Likelihoods

compare_subject(): Information Criteria For Each Participant

model_averaging(): Model Averaging

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

Contrasts

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

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

fMRI modelling

MRI(): GLM model for fMRI data

MRI_AR1(): Create an AR(1) GLM model for fMRI data

convolve_design_matrix(): Convolve Events with HRF to Construct Design Matrices

design_fmri(): Create fMRI Design for EMC2 Sampling

high_pass_filter(): Apply High-Pass Filtering to fMRI Data

plot_design_fmri(): Plot fMRI Design Matrix

plot_fmri(): Plot fMRI peri-stimulus time courses

reshape_events(): Reshape events data for fMRI analysis

split_timeseries(): Split fMRI Timeseries Data by ROI Columns

trend modelling

make_trend(): Create a trend specification for model parameters

trend_help(): Get help information for trend kernels and bases

get_trend_pnames(): Get parameter types from trend object

Latent variable modelling

align_loadings(): Reorder MCMC Samples of Factor Loadings

cut_factors(): Cut Factors Based on Credible Loadings

factor_diagram(): Factor diagram plot #Makes a factor diagram plot. Heavily based on the fa.diagram function of the psych package.

miscellaneous

update2version(): Update EMC Objects to the Current Version

Included data

forstmann: Forstmann et al.'s Data

samples_LNR: LNR Model of Forstmann Data (First 3 Subjects)