
 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 fastdm).
 Heavily optimized likelihood functions for speed (Navarro & Fuss, 2009).
 Flexible creation of complex models tailored to specific hypotheses (e.g. estimation of separate driftrates 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.
 Builtin Bayesian hypothesis testing and several convergence and goodnessoffit diagnostics.
 Changes to previous version:
 Handling of outliers via mixture model. http://ski.clps.brown.edu/hddm_docs/howto.html#dealwithoutliers
 New model HDDMRegression to allow estimation of trialbytrial regressions with a covariate. http://ski.clps.brown.edu/hddm_docs/howto.html#estimatearegressionmodel
 New model HDDMStimulusCoding. http://ski.clps.brown.edu/hddm_docs/howto.html#codesubjectresponses
 New model HLBA  a hierarchical Linear Ballistic Accumulator model (hddm.HLBA).
 Posterior predictive quantile plots (see model.plot_posterior_quantiles()).
 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#hierarchicaldriftdiffusionmodelsusedinhddm)
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.
 Intertrial 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.

Priors: by default model will use informative priors
(see http://ski.clps.brown.edu/hddm_docs/methods.html#hierarchicaldriftdiffusionmodelsusedinhddm)
If you want uninformative priors, set
 HDDMRegression takes patsy model specification strings. See http://ski.clps.brown.edu/hddm_docs/howto.html#estimatearegressionmodel and http://ski.clps.brown.edu/hddm_docs/tutorial_regression_stimcoding.html#chaptutorialhddmregression
 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#dealwithoutliers
 New model HDDMRegression to allow estimation of trialbytrial regressions with a covariate. http://ski.clps.brown.edu/hddm_docs/howto.html#estimatearegressionmodel
 New model HDDMStimulusCoding. http://ski.clps.brown.edu/hddm_docs/howto.html#codesubjectresponses
 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 
New and improved HDDM model with the following changes:
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