# chi2sim
Chi-square test with Monte Carlo simulation for contingency tables.
The package and its documentation are still under construction.
## Description
The `chi2sim` package is a Python implementation that contains the translation of R's H tests for contingency tables when `simulate.p.value = TRUE`, originally written in C and based on Patefield's (1981) FORTRAN algorithm. The package provides a fast and reliable method to compute p-values for chi-square tests using Monte Carlo simulation.
## Installation
```bash
pip install chi2sim
```
## Usage
```python
import numpy as np
from chi2sim import chi2_cont_sim
# Example contingency table
table = np.array([
[10, 5],
[20, 15]
], dtype=int)
# Perform chi-square test with Monte Carlo simulation
result = chi2_cont_sim(table)
print(result)
```
## Features
- Similar to SciPy's `scipy.stats.chi2_contingency`, but returns
- Monte Carlo simulation for p-value approximation
- Easy-to-use Python interface
## Requirements
- Python >= 3.9
- NumPy >= 1.15.0
## License
This project is licensed under the MIT License - see the LICENSE file for details.
## Citation
If you use the `chi2sim` package in your study, please don't forget to cite the following literatures:
- Hope, A. C. A. (1968). A simplified Monte Carlo significance test procedure. Journal of the Royal Statistical Society Series B, 30, 582–598. doi:10.1111/j.2517-6161.1968.tb00759.x.
- Patefield, W. M. (1981). Algorithm AS 159: An efficient method of generating r x c tables with given row and column totals. Applied Statistics, 30, 91–97. doi:10.2307/2346669.
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