Bayesian Hierarchical Analysis of Cognitive Models of Choice
Reorder MCMC Samples of Factor Loadings
Automatically Thin an emc Object
MCMC Chain Iterations
Convergence Checks for an emc Object
Information Criteria For Each Participant
Information Criteria and Marginal Likelihoods
Anova Style Contrast Matrix
Contrast Enforcing Equal Prior Variance on each Level
Contrast Enforcing Decreasing Estimates
Contrast Enforcing Increasing Estimates
Convolve Events with HRF to Construct Design Matrices
Posterior Credible Interval Tests
Posterior Quantiles
Cut Factors Based on Credible Loadings
The Diffusion Decision Model
The GNG (go/nogo) Diffusion Decision Model
Create fMRI Design for EMC2 Sampling
Specify a Design and Model
EMC2: Bayesian Hierarchical Analysis of Cognitive Models of Choice
Effective Sample Size
Factor diagram plot #Makes a factor diagram plot. Heavily based on the...
Model Estimation in EMC2
Gelman-Rubin Statistic
Bayes Factors
Get Data
Get Design
Get Group Design
Filter/Manipulate Parameters from emc Object
Get Prior
Get parameter types from trend object
Create Group-Level Design Matrices
Apply High-Pass Filtering to fMRI Data
Within-Model Hypothesis Testing
Initialize Chains
The Linear Ballistic Accumulator model
The Log-Normal Race Model
Simulate Data
Make an emc Object
Generate Subject-Level Parameters
Make SEM Diagram
Define Structural Equation Model (SEM) Matrices
Create a trend specification for model parameters
Parameter Mapping Back to the Design Factors
Merge Samples
Model Averaging
Create an AR(1) GLM model for fMRI data
GLM model for fMRI data
Plot Within-Chain Correlations
Return Data Frame of Parameters
Plot conditional accuracy functions
Plot Defective Cumulative Distribution Functions
Plot Difference of Cumulative Distribution Functions
Plot Defective Densities
Plot fMRI Design Matrix
Plot Design
Plot fMRI peri-stimulus time courses
Plots Density for Parameters
Plot Group-Level Relations
Plot the ECDF Difference in SBC Ranks
Plot the Histogram of the Observed Rank Statistics of SBC
Plot Inhibition Functions
Plot Mean SRRT
Plot Statistics on Data
Plots trends over time
Plot method for emc.design objects
Plot a prior
Plot Function for emc Objects
Generate Posterior/Prior Predictives
Prior Specification Information
Specify Priors for the Chosen Model
Likelihood Profile Plots
The Racing Diffusion Model
Recovery Plots
Register a custom C++ trend kernel
Reshape events data for fMRI analysis
Rotate loadings based on posterior median
Estimating Marginal Likelihoods Using WARP-III Bridge Sampling
Fine-Tuned Model Estimation
Run a Group-level Model.
Simulation-Based Calibration
Get Model Parameters from a Design
Gaussian Signal Detection Theory Model for Binary Responses
Split fMRI Timeseries Data by ROI Columns
Shorten an emc Object
Summary method for emc.design objects
Summary method for emc.group_design objects
Summary method for emc.prior objects
Summary Statistics for emc Objects
Get help information for trend kernels and bases
Update EMC Objects to the Current Version
Fit Bayesian (hierarchical) cognitive models using a linear modeling language interface using particle Metropolis Markov chain Monte Carlo sampling with Gibbs steps. The diffusion decision model (DDM), linear ballistic accumulator model (LBA), racing diffusion model (RDM), and the lognormal race model (LNR) are supported. Additionally, users can specify their own likelihood function and/or choose for non-hierarchical estimation, as well as for a diagonal, blocked or full multivariate normal group-level distribution to test individual differences. Prior specification is facilitated through methods that visualize the (implied) prior. A wide range of plotting functions assist in assessing model convergence and posterior inference. Models can be easily evaluated using functions that plot posterior predictions or using relative model comparison metrics such as information criteria or Bayes factors. References: Stevenson et al. (2024) <doi:10.31234/osf.io/2e4dq>.
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