ABI (Approximate Bayesian Inference) Module: Complete neural network-based parameter estimation workflow
abi_train(): Train neural estimators using
simulation-based inferenceabi_estimate(): Obtain point estimates from trained
modelsabi_assess(): Assess trained estimator performanceabi_sample_posterior(): Sample from posterior
distributionbuild_abi_input() with theta and Z outputs,
test set supportABC helpers: Add abc_abc() and
abc_cv() wrappers for ABC fitting and
cross-validation
Posterior predictive workflows: Add
abc_posterior_predictive_check(),
abi_posterior_predictive_check(), and
update_config_from_posterior() for teaching-oriented
posterior simulation workflows
Visualization:
plot_cv_recovery() methods for ABI models
(eam_abi_assess and eam_abi_posterior_samples
classes)plot_rt() now displays simulated RTs as densities and
observed RTs as histogramsPosterior summarization:
summarise_posterior_parameters() for aggregating posterior
samples
init_julia_env() for
neural network backendinst/julia/env/
for ABI setuptibble dependency for improved output
formattingbuild_abi_input function to create input for ABI
anlysis from EAM simulation output.summarise_by() to handle invalid column names
returned by summary functions (e.g., quantile functions returning “90%”,
“95%”). Now uses vctrs::vec_as_names() for proper name
repair.plot_posterior_parameters to the hist graph.plot_rt
to reflect the median RT within each condition.