Description:

Purpose

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.

Features

• 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. http://ski.clps.brown.edu/hddm_docs/howto.html#deal-with-outliers
• New model HDDMRegression to allow estimation of trial-by-trial regressions with a covariate. http://ski.clps.brown.edu/hddm_docs/howto.html#estimate-a-regression-model
• New model HDDMStimulusCoding. http://ski.clps.brown.edu/hddm_docs/howto.html#code-subject-responses
• 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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