Package: eam 1.2.2

eam: Evidence Accumulation Models

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 Levy 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, facilitating the study of a wide range of cognitive processes in tasks involving perceptual decision making, memory retrieval, and value-based decision making. Key methods implemented in the package are described in Ratcliff (1978) <doi:10.1037/0033-295X.85.2.59>, Usher and McClelland (2001) <doi:10.1037/0033-295X.108.3.550>, Brown and Heathcote (2008) <doi:10.1016/j.cogpsych.2007.12.002>, Tillman, Van Zandt and Logan (2020) <doi:10.3758/s13423-020-01719-6>, Wieschen, Voss and Radev (2020) <doi:10.20982/tqmp.16.2.p120>, Csilléry, François and Blum (2012) <doi:10.1111/j.2041-210X.2011.00179.x>, Beaumont (2019) <doi:10.1146/annurev-statistics-030718-105212>, and Sainsbury-Dale, Zammit-Mangion and Huser (2024) <doi:10.1080/00031305.2023.2249522>.

Authors:Guangyu Zhu [aut], Guang Yang [aut, cre]