Hddm

Latest version: v1.0.1

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0.5.5

===========================

* Upgrade dependency to pymc 2.3.3
* Remove LBA model as likelihood seems broken

0.5.3

===========================

* Compatibility with pandas > 0.13.
* Fix problem that causes stats to not be generated when
loading model.
* Update packages to work with anaconda 1.9.

0.5.2

===========================

* Refactored posterior predictive plots and added tutorial:
http://ski.clps.brown.edu/hddm_docs/tutorial_post_pred.html
* Smaller bugfixes.
* Works with PyMC 2.3.
* Experimental features:
* Updated HLBA model but currently has bad recovery.
* Added sample_emcee() to use the emcee parallel sampler.
Seems to work but requires some tuning and does not seem
to beat slice sampling.

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.

0.4.1

==========

* Models are now pickable.
(This means they can be loaded and saved.
Critically, it is now also trivial to run multiple
models in parallel that way.)

0.4

LICENSE file) instead of GPLv3.

New features
------------

* 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()).

Bugfixes
--------

* model.load_db() is working again.

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