Evidence Accumulation Models
Bootstrap resample ABC posterior samples
ABC model comparison wrapper
ABC with resampling
Simulate evidence accumulation in a two-bound drift-diffusion model
Simulate evidence accumulation in a drift-diffusion model
Simulate evidence accumulation in a leaky competing accumulator model ...
Internal function to apply a spec to data
Build input for Approximate Bayesian Computation (ABC)
Build input for Amortized Bayesian Inference (ABI)
Extract and format parameter matrix for ABI
Extract and format summary statistics for ABI
Calculate total number of rows needed for flattened data
Backend detector for 2-boundary DDM
Backend detector for standard DDM
Backend detector for LCA-GI
Evaluate a list of formulas sequentially with data
Extract parameter values from abc result
Extract posterior medians from abc_resample output
Fill pre-allocated data.table with simulation results
Convert simulation results to a tidy data.table
Get all registered backend detectors
Extract column names from simulation results
Extract all left-hand side variable names from config formulas and pri...
Initialize simulation output directory structure
Rebuild eam_simulation_output from an existing output directory
Heuristic to determine if parallel processing should be used
Process a single chunk for map_by_condition
Map a function by condition across simulation output chunks
Heuristic to calculate optimal chunk size for simulation configuration
Create a new simulation configuration
Create a eam_simulation_output object
Plot accuracy for DDM-2B model (internal)
Plot accuracy for DDM model (internal)
Plot accuracy graph (internal)
Plot accuracy comparison between posterior and observed data
Plot CV parameter pair correlations
Plot CV parameter recovery
Plot parameter posterior distributions
Plot resample forest plots
Plot resample median distributions
Plot reaction time distributions
Add two summarise_by specs together
Join two eam_summarise_by_tbl objects
Pre-allocate data.table columns with appropriate data types
Print method for eam simulation configuration
Helper to resolved defined symbols in our formulas
Route model alias to backend and enrich configuration
Run a chunk of simulation conditions and save results to disk
Run a given condition with multiple trials
Run a full simulation across multiple conditions in parallel
Run a full simulation across multiple conditions (serial version)
Run a simulation with specified configuration
Run a single trial of the 2-boundary DDM simulation
Run a single trial of the DDM simulation
Run a single trial of the LCA-GI simulation
Internal function to perform the core summarise_by logic
Summarise data by groups with optional pivoting
Summarise posterior parameter distributions
Summarise resample medians
Simulation-based evidence accumulation models for analyzing responses and reaction times in single- and multi-response tasks. The package includes simulation engines for five representative models: the Diffusion Decision Model (DDM), Leaky Competing Accumulator (LCA), Linear Ballistic Accumulator (LBA), Racing Diffusion Model (RDM), and Levy Flight Model (LFM), and extends these frameworks to multi-response settings. The package supports user-defined functions for item-level parameterization and the incorporation of covariates, enabling flexible customization and the development of new model variants based on existing architectures. Inference is performed using simulation-based methods, including Approximate Bayesian Computation (ABC) and Amortized Bayesian Inference (ABI), which allow parameter estimation without requiring tractable likelihood functions. In addition to core inference tools, the package provides modules for parameter recovery, posterior predictive checks, and model comparison, facilitating the study of a wide range of cognitive processes in tasks involving perceptual decision making, memory retrieval, and value-based decision making. Key methods implemented in the package are described in Ratcliff (1978) <doi:10.1037/0033-295X.85.2.59>, Usher and McClelland (2001) <doi:10.1037/0033-295X.108.3.550>, Brown and Heathcote (2008) <doi:10.1016/j.cogpsych.2007.12.002>, Tillman, Van Zandt and Logan (2020) <doi:10.3758/s13423-020-01719-6>, Wieschen, Voss and Radev (2020) <doi:10.20982/tqmp.16.2.p120>, Csilléry, François and Blum (2012) <doi:10.1111/j.2041-210X.2011.00179.x>, Beaumont (2019) <doi:10.1146/annurev-statistics-030718-105212>, and Sainsbury-Dale, Zammit-Mangion and Huser (2024) <doi:10.1080/00031305.2023.2249522>.