Hypothesize

Spec sheet

Purpose

Hypothesize is a Python library for robust statistics that compares groups and measures associations.Read 11 Oct 2026 · alcampopiano.github.io

Deployment

desktopRead 23 Sep 2026 · alcampopiano.github.io

Free plan

YesRead 23 Sep 2026 · alcampopiano.github.io

Support

The home page directs users to GitHub Issues for questions and bug reports.Read 11 Oct 2026 · alcampopiano.github.io

License

Hypothesize is licensed under the BSD 3-Clause license.Read 11 Oct 2026 · alcampopiano.github.io

Founded

2020Read 19 Oct 2026 · alcampopiano.github.io

Academic citation

The site requests that academic users cite Campopiano and Wilcox's 2020 Journal of Open Source Software article.Read 11 Oct 2026 · alcampopiano.github.io

Citation

The home page gives a 2020 Journal of Open Source Software citation with Allan Campopiano and Rand R. Wilcox as authors.Read 11 Oct 2026 · alcampopiano.github.io

Data import formats

Pandas DataFrames or Series; CSV via PandasRead 23 Sep 2026 · alcampopiano.github.io

Dependencies

The listed dependencies are Python 3.6 or newer, NumPy, Pandas, SciPy, and more-itertools, installed automatically with the installation command.Read 11 Oct 2026 · alcampopiano.github.io

Error control

Family-wise error can be controlled with sequentially rejective methods.Read 11 Oct 2026 · alcampopiano.github.io

Estimator options

Where applicable, tests can use estimators such as the mean, median, trimmed mean, winsorized correlation, and percentage bend correlation.Read 11 Oct 2026 · alcampopiano.github.io

Estimators

Where applicable, tests let users specify estimators such as the mean, median, trimmed mean, winsorized correlation, and percentage bend correlation.Read 11 Oct 2026 · alcampopiano.github.io

Examples

The overview says every top level function has a one click, ready to run example.Read 11 Oct 2026 · alcampopopiano.github.io

Installation

The package can be installed with pip using `pip install hypothesize`.Read 11 Oct 2026 · alcampopiano.github.io

Methods

The library brings robust statistical methods based on Rand R. Wilcox’s WRS package to Python.Read 11 Oct 2026 · alcampopiano.github.io

Notable function limit

The function reference says some documented tests currently support only `trim_mean` as their measure-of-location estimator.Read 11 Oct 2026 · alcampopiano.github.io

Notable limitation

The function reference says `l2drmci` cannot handle unequal sample sizes when `pairwise_drop_na` is false.Read 11 Oct 2026 · alcampopiano.github.io

Notebook integration

The GitHub README says each statistical test can be launched in Deepnote’s hosted notebook environment with sample data.Read 11 Oct 2026 · github.com

Regression models

NoRead 23 Sep 2026 · alcampopiano.github.io

Requirements

The listed dependencies are Python 3.6 or newer, NumPy, Pandas, SciPy, and more-itertools; the page says they install automatically with the pip command.Read 11 Oct 2026 · alcampopiano.github.io

Scripting support

YesRead 23 Sep 2026 · alcampopiano.github.io

Statistical designs

It supports single- and multi-factor designs, including independent, dependent, and mixed groups.Read 11 Oct 2026 · alcampopiano.github.io

Statistical tests

YesRead 23 Sep 2026 · alcampopiano.github.io

Use

The library provides methods for comparing groups and measuring associations.Read 11 Oct 2026 · alcampopiano.github.io

Where Specifiction places it

• Statistical Analysis Software#67 of 1067.5