Project details for HDDM

Screenshot HDDM 0.2

by Wiecki - March 15, 2012, 01:45:41 CET [ Project Homepage BibTeX Download ]

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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:

Initial Announcement on mloss.org.

BibTeX Entry: Download
URL: Project Homepage
Supported Operating Systems: Agnostic
Data Formats: Csv
Tags: Bayesian Estimation, Drift Model, Hierarchical
Archive: download here

Other available revisons

Version Changelog Date
0.5
  • New and improved HDDM model with the following changes:
    • Priors: by default model will use informative priors (see http://ski.clps.brown.edu/hddm_docs/methods.html#hierarchical-drift-diffusion-models-used-in-hddm) If you want uninformative priors, set informative=False.
    • Sampling: This model uses slice sampling which leads to faster convergence while being slower to generate an individual sample. In our experiments, burnin of 20 is often good enough.
    • Inter-trial variablity parameters are only estimated at the group level, not for individual subjects.
    • The old model has been renamed to HDDMTransformed.
    • HDDMRegression and HDDMStimCoding are also using this model.
  • HDDMRegression takes patsy model specification strings. See http://ski.clps.brown.edu/hddm_docs/howto.html#estimate-a-regression-model and http://ski.clps.brown.edu/hddm_docs/tutorial_regression_stimcoding.html#chap-tutorial-hddm-regression
  • Improved online documentation at http://ski.clps.brown.edu/hddm_docs
  • A new HDDM demo at http://ski.clps.brown.edu/hddm_docs/demo.html
  • Ratcliff's quantile optimization method for single subjects and groups using the .optimize() method
  • Maximum likelihood optimization.
  • Many bugfixes and better test coverage.
  • hddm_fit.py command line utility is depracated.
April 24, 2013, 02:53:07
0.4
  • 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()).
November 26, 2012, 19:49:49
0.2

Initial Announcement on mloss.org.

March 15, 2012, 01:45:41

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