miaojingang/irrcac

Chance-corrected Agreement Coefficients

Chance-corrected Agreement Coefficients

The irrCAC is a Python package that provides several functions for calculating various chance-corrected agreement coefficients. This package closely follows the general framework of inter-rater reliability assessment presented by Gwet (2014).

The functionality covers calculations for various chance-corrected agreement coefficients (CAC) among 2 or more raters. Among the CAC coefficients covered are Cohen's kappa, Conger's kappa, Fleiss' kappa, Brennan-Prediger coefficient, Gwet's AC1/AC2 coefficients, and Krippendorff's alpha. Multiple sets of weights are proposed for computing weighted analyses.

The functions included in this package can handle 2 types of input data. Those types with the corresponding coefficients are in the following list:

1. Contingency Table

1. Brennar-Prediger
2. Cohen's kappa
3. Gwet AC1/AC2
4. Krippendorff's Alpha
5. Percent Agreement
6. Schott's Pi

2. Raw Data

1. Fleiss' kappa
2. Gwet AC1/AC2
3. Krippendorff's Alpha
4. Conger's kappa
5. Brennar-Prediger

Note

All of these statistical procedures are described in details in Gwet, K.L. (2014,ISBN:978-0970806284): "Handbook of Inter-Rater Reliability," 4th edition, Advanced Analytics, LLC.

This package is a port (with permission) to Python of the irrCAC library for R by Gwet, K.L.

Important

This is a work in progress and does not have (yet) the full functionality found in the R library.

Installation

To install the package, run:

pip install irrCAC

Developers

To use the code for development it is recommended to install poetry and run:

poetry install

And add the pre-commit hook:

pre-commit install

and update the hooks:

pre-commit autoupdate

To update the project dependencies, run:

poetry update

Next run the tests:

poetry run pytest

There is also a config file for tox so you can automatically run the tests for various python versions like this:

tox

Documentation

The documentation of the project is available at the following page: http://irrcac.readthedocs.io/