Project details for HDDM

Screenshot HDDM 0.4

by Wiecki - November 26, 2012, 19:49:49 CET [ Project Homepage BibTeX Download ]

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HDDM is a python toolbox for hierarchical Bayesian parameter estimation of the Drift Diffusion Model (via PyMC). Drift Diffusion Models are used widely in psychology and cognitive neuroscience to study decision making.


  • Uses hierarchical Bayesian estimation (via PyMC) of DDM parameters to allow simultaneous estimation of subject and group parameters, where individual subjects are assumed to be drawn from a group distribution. HDDM should thus produce better estimates when less RT values are measured compared to other methods using maximum likelihood for individual subjects (i.e. DMAT or fast-dm).
  • Heavily optimized likelihood functions for speed (Navarro & Fuss, 2009).
  • Flexible creation of complex models tailored to specific hypotheses (e.g. estimation of separate drift-rates for different task conditions; or predicted changes in model parameters as a function of other indicators like brain activity).
  • Easy specification of models via configuration file fosters exchange of models and research results.
  • Built-in Bayesian hypothesis testing and several convergence and goodness-of-fit diagnostics.
Changes to previous version:
  • Handling of outliers via mixture model.
  • New model HDDMRegression to allow estimation of trial-by-trial regressions with a covariate.
  • New model HDDMStimulusCoding.
  • New model HLBA -- a hierarchical Linear Ballistic Accumulator model (hddm.HLBA).
  • Posterior predictive quantile plots (see model.plot_posterior_quantiles()).
BibTeX Entry: Download
Supported Operating Systems: Agnostic
Data Formats: Csv
Tags: Bayesian Estimation, Drift Model, Hierarchical
Archive: download here


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