---
id: momentchi2
version: "0.1.8"
license: unclear
license_treatment: permissive
maintenance: abandoned
---

# momentchi2 — A collection of methods for computing the cdf of a weighted sum of chi-squared random variables.

License: permissive · Maintenance: abandoned · Downloads: 276.6K/mo

## What it is and what it does

momentchi2 provides four statistical approximation methods for computing the cumulative distribution function (CDF) of a weighted sum of chi-squared random variables. This is a specialized numerical problem in statistics where exact computation is expensive; the package trades exact accuracy for speed using moment-matching techniques. The four methods—Hall-Buckley-Eagleson, Satterthwaite-Welch, Wood's F, and Lindsay-Pilla-Basak—each have different strengths: Hall-Buckley-Eagleson is recommended for larger coefficient sets (over 100), while Lindsay-Pilla-Basak works better for smaller sets (up to 10). The package wraps scipy and numpy to deliver these approximations.

You call one of the four functions with a coefficient vector and quantile value(s), and it returns the approximate CDF. Input can be a scalar, list, or numpy array; output format matches input format. The Lindsay-Pilla-Basak method accepts a tunable parameter p (default 4) to trade accuracy for speed. The package is a Python port of the R package momentchi2 and is intended for researchers and practitioners who need fast, reasonably accurate CDF approximations rather than exact solutions.

Use it for:

- Approximate CDF of weighted chi-squared sums in statistical hypothesis testing or variance component estimation.
- Fast numerical approximation in simulation studies where exact computation would be prohibitively slow.
- Selecting among four moment-matching methods based on the size of your coefficient vector and accuracy requirements.
- Batch evaluation of CDF at multiple quantile points using numpy arrays for vectorized performance.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Computes the cumulative distribution function of a weighted sum of chi-squared random variables using four moment-matching approximation methods: Hall-Buckley-Eagleson, Satterthwaite-Welch, Wood's F, and Lindsay-Pilla-Basak.

Yes, if you need a fast approximate CDF for weighted chi-squared sums and can tolerate two to three decimal place accuracy. No, if you require exact results, active maintenance, or modern package support—the project is abandoned (last release 2021-08-27) and may have compatibility issues with current scipy/numpy versions. Suitable for research or one-off analysis, risky for production systems.

## Install

pip install momentchi2
uv add momentchi2
poetry add momentchi2

## Installing momentchi2

Before you install: Low install friction with only numpy and scipy as dependencies. However, the package is abandoned—last release was 2021-08-27 with no updates since, so expect no maintenance or bug fixes.

License in practice: MIT license (permissive) means you can use, modify, and distribute the package freely with minimal restrictions, though you must include the license notice.

Quickstart:

pip install momentchi2

from momentchi2 import hbe
import numpy as np

# Compute CDF for weighted chi-squared sum
result = hbe(np.array([1.5, 1.5, 0.5, 0.5]), np.array([0.627, 10.203]))

Requires Python 3.6 or later; numpy and scipy must be installed.

Verify before relying:

- Whether the package's accuracy claims (two to three decimal places) hold for your specific use case and coefficient configuration.
- Current compatibility with modern scipy and numpy versions, given the package has not been updated since 2021.

## Package facts

- License: not declared (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: abandoned
- Downloads: 276.6K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

chi-squared weighted sum cdf, moment matching approximation, chi-squared distribution, statistical approximation methods, quadratic form distribution, weighted chi-squared cdf, statistical-approximation, chi-squared, moment-matching

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