{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/3"}],"enrichment":{"capability":"Reliability engineering and survival analysis library that fits probability distributions to data, models failure mechanisms, and provides tools for accelerated life testing and repairable systems analysis.","skillfed_tags":["survival-analysis","weibull-distribution","accelerated-life-testing"],"use_cases":["Fit Weibull or other distributions to product failure data and estimate reliability metrics like mean time to failure.","Analyze survival curves from clinical or industrial time-to-event studies with censored observations using Kaplan-Meier estimation.","Model accelerated life testing experiments with temperature, voltage, or stress as covariates across multiple distributions.","Calculate stress-strength interference probabilities to assess design margin between applied stress and material strength.","Generate probability plots and goodness-of-fit statistics to compare competing distribution models for a dataset."],"what_it_does":"Reliability is a Python library for reliability engineering and survival analysis that extends scipy.stats with specialized tools for fitting probability distributions, modeling failure mechanisms, and analyzing time-to-failure data. It supports Weibull, Exponential, Gamma, Gumbel, Normal, Lognormal, Loglogistic, and Beta distributions, with capabilities for handling right-censored data, mixture models, and competing risks. The library includes non-parametric survival estimation (Kaplan-Meier, Nelson-Aalen), goodness-of-fit testing, and interactive visualization via matplotlib.\n\nThe package is designed for engineers and researchers working with reliability data who need tools otherwise available only in proprietary software. It covers accelerated life testing with multiple life-stress models, physics-of-failure analysis, repairable systems modeling, and reliability growth analysis. Dependencies on autograd, scipy, numpy, matplotlib, and pandas provide the numerical and visualization backbone.","worth_installing":"Yes, if you work in reliability engineering, quality assurance, or survival analysis and need distribution fitting and specialized statistical tools. The library is production-stable with no known vulnerabilities and low install friction. The LGPLv3 copyleft license requires derivative works to remain open-source. Maintenance is aging (525 days since last release), so verify that the feature set and numerical methods meet your current needs before committing to a long-term dependency."},"id":"reliability","links":{"html":"https://skillfed.io/packages/reliability","md":"https://skillfed.io/packages/reliability.md","pypi":"https://pypi.org/project/reliability/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2025-03-07","license_spdx":null,"license_treatment":"copyleft","name":"reliability","python_support":"supports_current","summary":"Reliability Engineering toolkit for Python"},"popularity":{"monthly_downloads":1049051,"position":4446,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.9.0"}