{"categories":[{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/3"}],"enrichment":{"capability":"Computes Hartigan's dip test statistic and p-value to detect multimodality in a sample, measuring how far an empirical distribution deviates from unimodality.","skillfed_tags":["statistical-testing","distribution-analysis"],"use_cases":["Test whether a univariate sample is unimodal or contains multiple modes, useful in exploratory data analysis.","Detect bimodality or multimodality in biological measurements, particle size distributions, or other empirical samples.","Validate assumptions in statistical modeling pipelines that require unimodal data.","Compare modality across groups or conditions by computing dip statistics for each sample.","Perform hypothesis testing on the null hypothesis of unimodality without assuming a specific parametric form."],"what_it_does":"diptest is a Python/C++ implementation of Hartigan & Hartigan's statistical test for unimodality. It computes a dip statistic that measures the maximum vertical distance between an empirical cumulative distribution function and the best-fitting unimodal distribution, making no assumptions about the null distribution's form beyond unimodality.\n\nThe package exposes two main functions: `dipstat` computes only the dip statistic, while `diptest` returns both the statistic and a p-value. P-values can be computed via interpolation of a critical value table (default, fast) or by bootstrapping the null hypothesis (slower but more accurate for non-standard cases). Bootstrapping supports optional OpenMP-based parallelization for speed.","worth_installing":"Yes, if you need to test for unimodality in univariate samples. The package is actively maintained, has no known vulnerabilities, installs easily on modern Python versions via pre-built wheels, and implements a well-established statistical test. Copyleft licensing is a consideration only if you plan to redistribute modified code; using it as a library in a proprietary application is permissible. Medium install friction is negligible for most users thanks to wheel availability."},"id":"diptest","links":{"html":"https://skillfed.io/packages/diptest","md":"https://skillfed.io/packages/diptest.md","pypi":"https://pypi.org/project/diptest/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-04-24","license_spdx":null,"license_treatment":"copyleft","name":"diptest","python_support":"supports_current","summary":"Hartigan's diptest."},"popularity":{"monthly_downloads":192953,"position":9855,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.11.0"}