Python implementation of the R package IRR

## Project description

Python implementation of the R package IRR, all credit goes to the original authors [1]. The package contains functions to calculate coefficients of Interrater Reliability and Agreement for interval, ordinal and nominal data: intraclass-correlations, Finn-Coefficient, Robinson’s A, Kendall’s W, Cohen’s Kappa, and others. This is a straight line-for-line port from the R-package, so it is not particularly Pythonic and mainly made as an exercise to learn more about R. For documentation I highly recommend you head over to the R package page, they put in a lot of effort for the documentation!

## How to install

The package is available on the Python Package Index (PyPI). To install it you can run:

pip install pyirr

## How to use

A simple example:

from pyirr import read_data, intraclass_correlation

intraclass_correlation(data, "twoway", "agreement")

Returns:

==================================================
Intraclass Correlation Results
==================================================
Model: twoway
Type: agreement

Subjects = 20
Raters = 3
ICC(A,1) = 0.20

F-Test, H0: r0 = 0 ; H1 : r0 > 0
F(19.00,39.75) = 1.83, p = 0.0543

95%-Confidence Interval for ICC Population Values:
-0.039 < ICC < 0.494
==================================================

Another simple example:

from pyirr import read_data, kappam_fleiss

kappam_fleiss(data, detail=True)

Returns:

==================================================
Fleiss Kappa for m Raters
==================================================
Subjects = 30
Raters = 6
Kappa = 0.430

z = 17.652
p-value = 0.000

Kappa       z  p.value
1. Depression            0.245   5.192      0.0
2. Personality Disorder  0.245   5.192      0.0
3. Schizophrenia         0.520  11.031      0.0
4. Neurosis              0.471   9.994      0.0
5. Other                 0.566  12.009      0.0
==================================================

## Project details

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