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).
• Estimate trial-by-trial correlations between a brain measure (e.g. fMRI BOLD) and a diffusion model parameter using the HDDMRegression model.
• Built-in Bayesian hypothesis testing and several convergence and goodness-of-fit diagnostics.

Changes to previous version:

• 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.

BibTeX Entry: Download

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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