Changelog
Source:NEWS.md
eam (development version)
New Features
-
ABI (Approximate Bayesian Inference) Module: Complete neural network-based parameter estimation workflow
-
abi_train(): Train neural estimators using simulation-based inference -
abi_estimate(): Obtain point estimates from trained models -
abi_assess(): Assess trained estimator performance -
abi_sample_posterior(): Sample from posterior distribution - Enhanced
build_abi_input()with theta and Z outputs, test set support
-
ABC helpers: Add
abc_abc()andabc_cv()wrappers for ABC fitting and cross-validationPosterior predictive workflows: Add
abc_posterior_predictive_check(),abi_posterior_predictive_check(), andupdate_config_from_posterior()for teaching-oriented posterior simulation workflows-
Visualization:
- New
plot_cv_recovery()methods for ABI models (eam_abi_assessandeam_abi_posterior_samplesclasses) - Update posterior RT and accuracy plots to compare simulated and observed data more directly
-
plot_rt()now displays simulated RTs as densities and observed RTs as histograms
- New
Posterior summarization:
summarise_posterior_parameters()for aggregating posterior samples
Infrastructure
- Julia environment integration via
init_julia_env()for neural network backend - Add bundled Julia project files under
inst/julia/env/for ABI setup - Add
tibbledependency for improved output formatting - Improve simulation routing, including LFM support and updated LBA routing
eam 1.1.0
CRAN release: 2026-02-09
- Add
build_abi_inputfunction to create input for ABI anlysis from EAM simulation output. - Simulation allow more than 1024 data chunks/arrow partitions. Now, it depends on the hard limit of the arrow library and the file system.
- Fix
summarise_by()to handle invalid column names returned by summary functions (e.g., quantile functions returning “90%”, “95%”). Now usesvctrs::vec_as_names()for proper name repair. - By convention of ABC, change the prior of
plot_posterior_parametersto the hist graph. - By convention of ABC, change the posterior of
plot_rtto reflect the median RT within each condition.