fill_data_table

This function fills the pre-allocated data.table vectors with data from
simulation results, iterating through all conditions and trials.

internal

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>.

Guang Yang

Evidence Accumulation Models

fill_data_table function

<dl><dt>sim_results</dt>
<dd>The output from run_simulation(), a list of conditions</dd>
<dt>dt_lists</dt>
<dd>Named list of pre-allocated vectors for each column</dd>
<dt>trial_col_names</dt>
<dd>Character vector of trial column names</dd>
<dt>cond_param_names</dt>
<dd>Character vector of condition parameter names</dd>
<dt>first_trial_col</dt>
<dd>Name of the first trial column to use for item
counting</dd></dl>

Arguments

Fill pre-allocated data.table with simulation results — fill_data_table

<dl>

<dt>sim_results</dt>
<dd>The output from run_simulation(), a list of conditions</dd>


<dt>dt_lists</dt>
<dd>Named list of pre-allocated vectors for each column</dd>


<dt>trial_col_names</dt>
<dd>Character vector of trial column names</dd>


<dt>cond_param_names</dt>
<dd>Character vector of condition parameter names</dd>


<dt>first_trial_col</dt>
<dd>Name of the first trial column to use for item
counting</dd>

</dl>

fill_data_table: Fill pre-allocated data.table with simulation results

Description

Usage

Value

Arguments