eam

eam is a 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 Lévy 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. Overall, it facilitates the study of a wide range of cognitive processes in tasks involving perceptual decision making, memory retrieval, and value-based decision making.

Features

• ⚙️ High-Performance Backend
  • Pure C++ core with fast vectorization-friendly algorithms designed. 100x speedup compared with naive python implementations.
  • Memory-efficient computation, enabling more than 10 million trials on a standard desktop machine.
  • Parallel execution with near-linear speed-up across multiple CPU cores.