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.

• 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.

About: Pynopticon is a toolbox that allows you to create and train your own object recognition classifiers. It makes rapid prototyping of object recognition work flows a snap. Simply create a dataset of [...]

Initial Announcement on mloss.org.