About:
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.
Changes:

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.

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.
