hyppo
A comprehensive independence testing package
Decision gist · record as of 2026-08-14
Yes, if you need multivariate hypothesis testing for high-dimensional or nonlinear data. The package is actively maintained, has low install friction, carries a permissive MIT License, and fills a real gap in Python's statistical testing landscape. Alpha status indicates the API may evolve, so verify that the specific tests you need are documented and validated for your use case.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low friction installation with a pure-Python wheel.
- Active maintenance as of 2026-08-09 with recent commits.
- Depends on a substantial stack (numpy, scipy, scikit-learn, statsmodels, numba, autograd, pandas, patsy, future) but all are standard scientific Python packages.
License · maintenance · safety
MIT License (permissive) — MIT License permits commercial and private use with minimal restrictions—you may use, modify, and distribute hyppo freely provided you include the license notice.
last release 2025-05-24 (447 days) · last repo commit 2026-08-09 · 251 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 155,613 downloads/mo, #10,807 on PyPI
Alternatives
Verify before relying
pip install hyppo
from hyppo.independence import Hsic
import numpy as np
X = np.random.randn(10, 5)
Y = np.random.randn(10, 3)
stat, pval = Hsic().test(X, Y)- Whether the package's novel tests are documented with references or citations to support their statistical validity.
- Performance characteristics and scalability limits for very high-dimensional datasets.
- Specific use-case suitability and validation status for genomics, neuroimaging, or other domains.
What it is and what it does
hyppo is a Python library for multivariate hypothesis testing, designed to handle high-dimensional and nonlinear data where traditional univariate tests fall short. It implements a range of independence tests and other statistical procedures, including some novel methods not available in other packages. The library targets researchers and practitioners who need to test relationships between multiple variables simultaneously, filling a gap where R libraries exist but lack consistent interfaces or Python availability.
The package depends on core scientific Python tools (numpy, scipy, scikit-learn, statsmodels, numba, autograd, pandas, patsy, future) and is maintained actively. It runs on Python 3.8 through 3.14 and is in Alpha status, meaning the API may still evolve but the core functionality is usable for research and analysis work.
Use it for
- Test independence between variables in high-dimensional datasets across multiple fields.
- Validate nonlinear relationships in multivariate data where standard correlation is insufficient.
- Compare statistical test results using multiple hypothesis testing methods on the same data.
- Conduct exploratory data analysis when you need to detect associations in complex, multi-variable systems.
- Implement novel independence tests documented in recent statistical literature not yet in mainstream packages.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need multivariate hypothesis testing for high-dimensional or nonlinear data.
The package is actively maintained, has low install friction, carries a permissive MIT License, and fills a real gap in Python's statistical testing landscape. Alpha status indicates the API may evolve, so verify that the specific tests you need are documented and validated for your use case.
Install
hyppo on PyPI
Before you install
Low friction installation with a pure-Python wheel. Active maintenance as of 2026-08-09 with recent commits. Depends on a substantial stack (numpy, scipy, scikit-learn, statsmodels, numba, autograd, pandas, patsy, future) but all are standard scientific Python packages.
License in practice
MIT License permits commercial and private use with minimal restrictions—you may use, modify, and distribute hyppo freely provided you include the license notice.
Quickstart
pip install hyppo
from hyppo.independence import Hsic
import numpy as np
X = np.random.randn(10, 5)
Y = np.random.randn(10, 3)
stat, pval = Hsic().test(X, Y)Verify before relying
- Whether the package's novel tests are documented with references or citations to support their statistical validity.
- Performance characteristics and scalability limits for very high-dimensional datasets.
- Specific use-case suitability and validation status for genomics, neuroimaging, or other domains.
Package facts
| License | MIT License permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 9 packagesnumpyscipynumbascikit-learnautogradstatsmodelspatsypandasfuture |
| Maintenance | Actively maintained 447 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 155,613 / month, #10,807 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 3 - AlphaIntended Audience :: Science/ResearchLicense :: OSI Approved :: MIT LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Scientific/Engineering :: Mathematics |
Evidence: hyppo-0.5.2-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “multivariate hypothesis testing”
- hyppohyppo provides multivariate hypothesis testing for high-dimensional…
- pingouinPingouin provides a comprehensive statistical analysis toolkit for…
- statsmodelsstatsmodels provides statistical models, inference methods, and…
Give your agent the search over MCP, or paste the wish link into any chat.
More Mathematics packages
NetworkX provides data structures and algorithms for creating, analyzing, and manipulating graphs and networks, supporting everything from simple undirected graphs to complex directed and weighted networks.
kiwisolver is a Python binding to a fast C++ implementation of the Cassowary constraint solver, enabling you to solve systems of linear constraints and inequalities.
Install it if you need to solve constraint systems; skip it if you only need simple linear algebra.
SymPy is a Python library for symbolic mathematics, performing algebraic manipulation, calculus, equation solving, and mathematical expression simplification without numerical approximation.
ContourPy calculates contours of 2D quadrilateral grids using C++11 algorithms wrapped in Python, offering serial and multithreaded implementations without requiring Matplotlib as a dependency.
PyTorch provides GPU-accelerated tensor computation and automatic differentiation for building and training deep neural networks in Python.
onnxruntime loads and executes Open Neural Network Exchange (ONNX) models with a focus on inference performance across CPUs and accelerators.
Install it if you have ONNX models to run in production or development.
See also phik · pingouin · pyhdfe · copulas · pypots · diptest · powershap · statsmodels · hypothesis · scikit-posthocs