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>.

A 'Shiny' web application for energy industry analytics.
      Take an overview of the industry, measure Key Performance Indicators,
      identify changes in the industry over time, and discover new relationships in the data.