{"categories":[{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics"}],"enrichment":{"capability":"Detects change points in time series and signal data using exact and approximate algorithms for parametric and non-parametric models.","skillfed_tags":["time-series-analysis","signal-processing","statistical-methods"],"use_cases":["Detect regime changes in financial time series to identify market shifts or volatility transitions.","Segment sensor data or environmental measurements to identify periods of anomalous behavior.","Analyze neuroscience signals (e.g., acetylcholine regulation) to identify state transitions in biological systems.","Monitor agricultural or remote sensing data to identify changes in crop health or land use patterns.","Identify shifts in classroom engagement or student participation patterns in educational settings."],"what_it_does":"Ruptures is a Python library for offline change point detection in time series and signal data. It provides a collection of algorithms\u2014both exact and approximate\u2014for identifying structural breaks in non-stationary signals under various statistical models. The library emphasizes a consistent, well-documented interface and modular design, allowing different detection algorithms and cost models to be combined and extended.\n\nThe package depends on numpy and scipy for numerical computation. It is designed for offline analysis (where the entire signal is available upfront) rather than streaming detection. Users instantiate an algorithm object (e.g., Pelt with a kernel model), fit it to their signal, and call predict() to retrieve estimated change point locations. The library handles both univariate and multivariate signals.","worth_installing":"Yes. Ruptures is actively maintained, has no known vulnerabilities, and offers a comprehensive, well-documented toolkit for a specialized but important problem. The permissive BSD license and broad Python version support (3.9\u20133.13) make it low-risk to adopt. Install it if you need to detect structural breaks in time series or signal data."},"id":"ruptures","links":{"html":"https://skillfed.io/packages/ruptures","md":"https://skillfed.io/packages/ruptures.md","pypi":"https://pypi.org/project/ruptures/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2025-09-10","license_spdx":null,"license_treatment":"permissive","name":"ruptures","python_support":"supports_current","summary":"Change point detection for signals in Python."},"popularity":{"monthly_downloads":1558626,"position":3764,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"1.1.10"}