Metadata-Version: 2.1
Name: kendall-w
Version: 0.0.2
Summary: Computes Kendall's coefficient of concordance
Home-page: https://github.com/ugolbck/kendall-w
Author: Ugo Loobuyck
Author-email: ugo.loobuyck@gmail.com
License: BSD
Download-URL: https://github.com/ugolbck/kendall-w/tarball/0.0.2
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.8
Description-Content-Type: text/markdown
Requires-Dist: pandas

kendall-w
==========

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Author: **Ugo Loobuyck**

Overview
--------

Computes Kendall's coefficient of concordance for inter-annotator agreement
in the case of item ranking.

Installation / Usage
--------------------

To install use pip:

    $ pip install kendall-w


Or clone the repo:

    $ git clone https://github.com/ugolbck/kendall-w.git
    $ python setup.py install


Example
-------

```python
from kendall_w import 

annotations = [
        [1, 1, 1, 2],
        [2, 2, 2, 3],
        [3, 3, 3, 1],
    ]

W = compute_w(annotations) # returns 0.4375 (fair overall agreement)
```

Contributions
-------------

*All contributions are welcome.*

TODO:

- Handle ```pandas.DataFrame``` as an input with the instructions in (https://github.com/ugolbck/kendall-w/blob/master/kendall_w/kendall_w.py)
- Write unit tests for compute_w function
- Write code and unit tests for Kendall's tau function ?

