{"categories":[{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis/2"}],"enrichment":{"capability":"Detects knee (elbow) points in curves using the Kneedle algorithm, returning the point of maximum curvature for a given set of x and y values.","skillfed_tags":["curve-analysis","optimization","data-exploration"],"use_cases":["Determine optimal cluster count in k-means clustering by finding the elbow in the inertia curve.","Identify the point of diminishing returns in performance tuning or resource allocation curves.","Analyze system behavior curves to detect where performance degrades or stabilizes.","Find inflection points in cost-benefit or accuracy-complexity trade-off curves.","Detect transition points in experimental or observational data where behavior changes."],"what_it_does":"Kneed is a Python library that implements the Kneedle algorithm for detecting knee or elbow points in curves\u2014the points where the curve's curvature is greatest. It takes arrays of x and y values and returns the x-coordinate of maximum curvature, along with the corresponding y value. The library handles both concave and convex curves, supports increasing and decreasing functions, and can automatically detect curve shape if you're unsure. It depends on numpy and scipy for numerical computation.\n\nThe package is designed for exploratory data analysis and optimization problems where you need to find the \"elbow\" in a curve\u2014a common task in clustering (determining optimal cluster count), performance tuning, and system behavior analysis. It includes utilities for generating test data, tuning sensitivity, and visualizing results with matplotlib (optional). The API is straightforward: instantiate KneeLocator with your data and curve parameters, then access the knee attribute.","worth_installing":"Yes. The package is actively maintained, has no known vulnerabilities, installs with minimal friction (only numpy and scipy), and solves a specific well-defined problem with a published algorithm. The main caveat is the unclear license status\u2014verify the actual license before use in proprietary contexts. For research, data analysis, and optimization tasks, it's a solid choice."},"id":"kneed","links":{"html":"https://skillfed.io/packages/kneed","md":"https://skillfed.io/packages/kneed.md","pypi":"https://pypi.org/project/kneed/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-03-20","license_spdx":null,"license_treatment":"unclear","name":"kneed","python_support":"supports_current","summary":"Knee-point detection in Python"},"popularity":{"monthly_downloads":697094,"position":5300,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.8.6"}