chi2sim 1.0.2

Chi-square test with Monte Carlo simulation

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

pip install chi2sim

Usage

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.