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* 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.