reRandomStats

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║  reRandomStats                                                   ║
║  « Re-randomisation statistics in the spirit of Fisher »         ║
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A comprehensive Python toolkit for re-randomisation statistics in the tradition of Sir Ronald A. Fisher. The package provides Fisher's resampling test with flexible test statistics, pairwise multi-group comparisons with multiple-testing correction, binomial proportion tests, and a unified interface to classical parametric and non-parametric hypothesis tests.

Features

• Fisher's Resampling Test — permutation-based two-sample test using mean, median, or sum differences as the test statistic. Supports exhaustive enumeration for small samples and random resampling for large ones.
• Fisher's Exact Test — wrapper for 2×2 contingency table analysis.
• Multi-Group Pairwise Testing — runs all (or user-specified) pairwise comparisons with automatic multiple-testing correction (Benjamini-Hochberg FDR, Bonferroni, Holm, and others via statsmodels).
• Binomial Proportion Tests — single-sample binomial test with Wilson confidence intervals, plus two-sample z-test and chi-square comparisons.
• Classical Hypothesis Tests — unified dispatcher for Mann-Whitney U, Kruskal-Wallis, Kolmogorov-Smirnov, Mood's Median, Wilcoxon Rank-Sum, independent t-test, and chi-square.
• Data I/O — CSV reader supporting German-locale semicolon-delimited files, with wide→long table conversion.

Installation

From source (recommended for development)

git clone https://github.com/zerotonin/rerandomstats.git
cd rerandomstats
pip install -e ".[dev]"

Via conda environment

conda env create -f environment.yml
conda activate rerandomstats
pip install -e .

Dependencies

Core: numpy, scipy, pandas, statsmodels, prettytable, tqdm

Quick Start

Two-sample Fisher resampling test

from rerandomstats import FisherResamplingTest

# Compare two groups using median differences
test = FisherResamplingTest(
    data_a=[1.2, 3.4, 2.1, 4.5, 3.3],
    data_b=[5.6, 7.8, 6.5, 8.9, 7.2],
    func='medianDiff',
    combination_n=20_000,
)
p_value = test.main()
print(f"p = {p_value:.4f}")

Multi-group pairwise comparisons with FDR correction

import numpy as np
from rerandomstats import MultiGroupTest

data   = list(np.random.randn(30))
groups = ['control'] * 10 + ['treatment_A'] * 10 + ['treatment_B'] * 10

mgt = MultiGroupTest(
    data=data,
    group=groups,
    test='Fisher:medianDiff',
    combination_n=20_000,
    correction_type='fdr_bh',
)
results_df = mgt.main()
print(results_df)

Fisher's exact test

from rerandomstats import FisherExactTest

test = FisherExactTest(data_a=(8, 2), data_b=(1, 5))
print(f"p = {test.main():.4f}")

Binomial proportion test

from rerandomstats import BinomialStats

bs = BinomialStats(heads=73, total_flips=100)
result = bs.binomial_test(base_rate=0.5)
print(f"p = {result.pvalue:.4f}")
print(bs.exact_ci())

Classical hypothesis tests via the unified interface

from rerandomstats import HypothesisTests

ht = HypothesisTests(
    data_a=[1, 2, 3, 4, 5],
    data_b=[6, 7, 8, 9, 10],
    func='MannWhitneyU',
)
print(f"p = {ht.main():.4f}")

Available Tests

Family	Test String	Description
Fisher	Fisher:medianDiff	Resampling test — median difference
Fisher	Fisher:meanDiff	Resampling test — mean difference
Fisher	Fisher:sumDiff	Resampling test — sum difference
Fisher	Fisher:exact	Fisher's exact test (2×2 table)
Binomial	Binomial:ztest	Two-sample proportions z-test
Binomial	Binomial:chi2	Two-sample proportions chi-square
hypo	hypo:MannWhitneyU	Mann-Whitney U test
hypo	hypo:KruskalWallis	Kruskal-Wallis H test
hypo	hypo:ChiSquare	Chi-square goodness of fit
hypo	hypo:Kolmogorov	Kolmogorov-Smirnov test
hypo	hypo:MoodMedian	Mood's median test
hypo	hypo:WilcoxonRankSum	Wilcoxon rank-sum test
hypo	hypo:IndependentT	Independent samples t-test

Documentation

Full API documentation is built with Sphinx and hosted at: https://zerotonin.github.io/rerandomstats/

To build locally:

cd docs
make html
open _build/html/index.html

Running Tests

pytest

Contributing

Contributions are welcome! Please open an issue or pull request on GitHub.

License

This project is licensed under the MIT License — see LICENSE for details.

Citation

If you use this software in your research, please cite it:

@software{geurten_rerandomstats,
  author    = {Geurten, Bart R.H.},
  title     = {{reRandomStats}: Re-randomisation Statistics Toolkit},
  url       = {https://github.com/zerotonin/rerandomstats},
  license   = {MIT},
}

Acknowledgements

We acknowledge Sir Ronald Aylmer Fisher for his pioneering work on the re-randomisation test and his foundational contributions to the field of statistics.

Maintainer

Bart R.H. Geurten — Department of Zoology, University of Otago, Dunedin, New Zealand.