Metadata-Version: 2.4
Name: SSTT-multinomial
Version: 1.0.0
Summary: Tool to evaluate the distribution of the maximum order statistic. Also generates visuals for the relevant paper.
Author-email: "Anthony E. D. Mobbs" <tony@mobbs.com.au>, "Redmond R. Scoble" <imashellio@gmail.com>
License-Expression: BSD-3-Clause
Project-URL: Homepage, https://github.com/AnthonyMobbs/exact-multinomial
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.21.0
Requires-Dist: scipy>=1.7.0
Requires-Dist: gmpy2>=2.1.0
Requires-Dist: platformdirs>=3.0.0
Requires-Dist: pandas>=1.3.0
Requires-Dist: matplotlib>=3.4.0
Requires-Dist: seaborn>=0.11.0
Requires-Dist: tqdm>=4.60.0
Dynamic: license-file

# SSTT-multinomial
---

## Installation

Install the package with `pip`:

```bash
pip install SSTT-multinomial
```

## Quick start

### 1. Perform the maximum order test

The `PandZ(urnMax, marbles, urns)` function calculates the probability that a
uniform multinomial distribution of `marbles` observations across `urns`
categories contains a category with `urnMax` or more observations. It returns
`(p_value, z_score, log_p_value)`.

```python
from SSTT_multinomial.mos import PandZ

p_value, z_score, log_p_value = PandZ(
	urnMax=10,
	marbles=100,
	urns=25,
)
```

### 2. Produce visualisations

The `run_all()` function generates the visualisations used in the *Beyond
Chi-Square* paper.

```python
from SSTT_multinomial.rainbow_multiprocess import run_all

run_all()
```

## Citation
If you use SSTT_multinomial in your research, please cite the original methodological validation paper:

Mobbs, A. E. D., Scoble, R. R. (2026). Beyond Chi-Square: An Exact Maximum Order Statistic Test for Acute Categorical Clustering. [DOI placeholder]

## Core references

Bonetti, M., Cirillo, P., & Ogay, A. (2019). Computing the exact distributions of some functions of the ordered multinomial counts: maximum, minimum, range and sums of order statistics. Royal Society Open Science, 6(10), 190198. https://doi.org/10.1098/rsos.190198

Mobbs, A. E. D., & Boag, S. (2024). A social science trust taxonomy with emergent vectors and symmetry. Frontiers in Psychology, 15, 1335020. https://doi.org/10.3389/fpsyg.2024.1335020
