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Package Overview

Documentation Overview

AccumulatR AccumulatR-package
AccumulatR: Simulate and fit evidence-accumulation models

Model Specification

Define accumulators, outcomes, components, triggers, and finalize a race model.

race_spec()
Start a race-model specification
add_accumulator()
Add an accumulator to a model
add_pool()
Pool several accumulators under a shared label
add_outcome()
Define an observed response
add_component()
Define a mixture component
set_parameters()
Control parameter grouping and names
add_trigger()
Add a shared absence trigger
set_mixture()
Control how mixture components are combined
finalize_model()
Finalize a model for simulation and fitting
complexity_metrics()
Inspect the size of a compiled likelihood plan

Outcome Expressions and Timing

Compose outcome rules and onset dependencies.

build_outcome_expr()
Build a response rule from a quoted expression
all_of()
Define a response that requires several processes to finish
first_of()
Define a response that occurs when the first listed process finishes
none_of()
Define the absence of an event
inhibit()
Define a response that is blocked by another process
after()
Start one accumulator after another process finishes

Model Preparation

Inspect model parameters and prepare trial-level parameter matrices.

prepare_data()
Prepare behavioral data for likelihood evaluation
par_names()
List the parameter names used by a model
build_param_matrix()
Create trial-level parameter values

Likelihood and Simulation

Build native likelihood contexts, simulate observations, and evaluate probabilities.

make_context()
Build a compiled likelihood context from a model
log_likelihood()
Evaluate log-likelihoods of behavioral data
simulate()
Simulate behavioral data from a model
response_probabilities()
Evaluate marginal response probabilities

Visualization

Plot and inspect model structure.

processing_tree()
Draw a processing tree for the responses in a model
plot_accumulators()
Plot accumulators and their timing relations