hyppo v0.1.0 Release Notes

hyppo 0.1.0 is the culmination of 8 months of hard work. It contains many new features, numerous bug-fixes, improved test coverage and better documentation. All users are encouraged to use this release instead of the mgcpy, which this package is replacing, are a large number of bug-fixes and optimizations. Moreover, our development attention will now shift to bug-fix releases on this branch, and on adding new features on the development master branch.

Release highlights (as compared to mgcpy):

  • New time series and discriminability modules
  • More user-friendly implementation of k-Sample Testing

This release requires Python 3.5+.

New features

Independence testing

Tests have been given a unique class as compared to mgcpy. Parallelization in this module makes tests faster and Hsic has been added as a standalone test within this package (uses exact equivalence implementation).

k-sample testing

k-Sample testing is now organized within a class that is modeled similarly to how independence tests are run. More information about the specifics of how this works can be found in the docs.

Time series

Time series based independence tests have been included as a separate module from independence tests.


Simulations have been included as a separate module and hyppo.sims now includes k-sample simulations and time series simulations.


Discriminability has been included from the r-mgc package and has been changed so it conforms to the hyppo API.


A benchmarks folder as been added that contains notebooks comparing statistical power, algorithm wall times, and test statistics comparisons between the algorithms and sometimes between the respective R implementations. Many of those notebooks have been condensed into tutorials in the documentation.

Other changes

API has been changed as compared to mgcpy and is modeled after scikit-learn with the inclusion of the base.py containing an abstract class within each module and energy with a .test method calculating test statistic in p-value.


This release contains work by the following people (contributed at least one patch to this release, names in alphabetical order by last name):

  • Jayanta Dey +
  • Sambit Panda +

A total of 2 people contributed to this release. People with a "+" by their names contributed a patch for the first time.