{"categories":[{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/2"}],"enrichment":{"capability":"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.","skillfed_tags":["statistical-approximation","chi-squared","moment-matching"],"use_cases":["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."],"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\u2014Hall-Buckley-Eagleson, Satterthwaite-Welch, Wood's F, and Lindsay-Pilla-Basak\u2014each 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.\n\nYou 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.","worth_installing":"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\u2014the 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."},"id":"momentchi2","links":{"html":"https://skillfed.io/packages/momentchi2","md":"https://skillfed.io/packages/momentchi2.md","pypi":"https://pypi.org/project/momentchi2/"},"maintenance":{"status":"abandoned"},"meta":{"latest_release":"2021-08-27","license_spdx":null,"license_treatment":"permissive","name":"momentchi2","python_support":"supports_current","summary":"A collection of methods for computing the cdf of a weighted sum of chi-squared random variables."},"popularity":{"monthly_downloads":276649,"position":8161,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.1.8"}