pbapply and abc from Suggests to Imports so they are installed with the package, which improves library installation experience and reduces confusion.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.