Connectionists: [ANN] HDDM 0.5 released

[Thomas Wiecki]

Hi,

We are very happy to announce the release of HDDM 0.5 (hierarchical
Bayesian estimation of the drift-diffusion model) which comes with a lot of
new features and enhancements. The main reason to upgrade is the new
default model that does very well in parameter recovery studies and has
much faster convergence.

Installation instructions can be found on our homepage:
http://ski.clps.brown.edu/hddm_docs/

To upgrade from an existing installation, type:
pip install -U --no-deps kabuki
pip install -U --no-deps hddm

Changes:

HDDM 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.

Best,
Thomas, Imri and Michael
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