Changelog

Note: People with a "+" by their names contributed a patch for the first time.

v0.2.1

Release date: 25 February 2021
Supports: Python 3.6+.

Bug Fixes:

Documentation:

  • Fix PyPi description rendering issues

  • Add more descriptive contirubting guidelines

  • Add ROLES.md for specification about maintainers

  • Added power computation for independence increasing sample size and dimension

  • Add benchmark section and move relevant examples there

  • Add base classes

Maintenance:

  • Fix Gaussian kernel to prevent division by 0

  • Add checks for Type I error

Authors:

  • Sambit Panda

v0.2.0

Release date: 08 February 2021
Supports: Python 3.6+.

New features:

Bug Fixes:

  • Fixed error check for k-sample tests to be between samples instead of within

  • Time series doesn't clip negative values

  • Fix docs not building on netlify

  • Fix p-value calculations for permutation tests to be more in line with literature

  • Fix hyppo.independence.Dcorr and hyppo.independence.Hsic incorrect stats

Documentation:

  • Update badges and README to FIRM guidelines

  • Incorrect equation in hyppo.tools.circle docstring

  • Update README to be in line with scikit-learn

  • Remove literature reference section in docstrings, add links to papers

  • Add docstrings for hyppo.tools functions

  • Add overview.py file for an overview of the package

  • Add tutorials folder, rewrite so it is more user-friendly (port independence, k-sample, time series)

  • Add examples folder with more information about unique features

  • Move to sphinx-gallery instead of nbconvert

  • Use automodule instead of autoclass

  • Make clear about the package requirements and docs requirements

  • Make changelog into a single file

  • Add external links to neurodata and code of conduct

  • Add citing page to cite the package papers

  • Make index page a clone of README

  • Update MakeFile for more options

  • Add intersphinx mapping with links externally (numpy, scipy, etc.) and internally

  • Add docs for statistic function

  • Add discriminability tutorial

Maintenance:

  • Fix typos in warning commits

  • Updated tests to precalculate distance matrix

  • Moved from Travis CI to Circle CI

  • Raise base requirements.txt to fix failing tests on CircleCI

  • Add code coverage config files

  • Add documentation folders and files to .gitignore

  • Remove reps warning test

  • Cache numba after first call to speed up runs

  • Fix netlify config to new doc build structure

Authors:

  • Sambit Panda

  • Vivek Gopalakrishnan +

  • Ronak Mehta

  • Ronan Perry +

v0.1.3

Release date: 24 July 2020
Supports: Python 3.6+.

Bug Fixes:

  • Prevent division by zero when calculating using default Gaussian median kernel

Maintenance:

Authors:

  • Benjamin Pedigo +

  • Anton Alayakin +

v0.1.2

Release date: 5 May 2020
Supports: Python 3.6+.

Bug Fixes:

  • Fixed MMD/k-sample Hsic not running

Authors:

  • Sambit Panda

v0.1.1

Release date: 28 April 2020
Supports: Python 3.6+.

Documentation:

  • arXiv badge added to docs.

  • OS/Software requirements and license changes updated in README

  • Reference docs and tutorials added to Time Series module

Maintenance:

  • Pearson, Spearman, and Kendall are no longer tests within the package.

  • Python 3.5 no longer supported.

  • sklearn.pairwise.pairwise_distances used instead of scipy.spatial.distance.cdist.

  • Null distribution added as a class atribute

  • Calculate kernel once before calculating p-value

  • Upper and lower-case inputs are available for indep_test

Authors:

  • Ronak Mehta +

  • Sambit Panda

  • Bijan Varjavand +

v0.1.0

Release date: 25 February 2020
Supports: Python 3.5+.

Note: as compared to `mgcpy`_

New features:

  • Parallelization added to all tests

  • hyppo.independence.Hsic is now a stand alone class

  • Simulations are given module, with new k-sample and time series modules

  • Discrimnability ported from r-mgc

  • Benchmarks folder added with relevant notebooks comparing implementations

Maintenance:

  • Modified scikit-learn API adopted (classes given unique files, organized in independence, k-sample, and time series modules.

Authors:

  • Jayanta Dey +

  • Sambit Panda +