{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/13"},{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/2"},{"label":"Physics","url":"https://skillfed.io/packages/category/scientific-engineering-physics"}],"enrichment":{"capability":"iminuit is a Python interface to MINUIT2 (CERN's C++ optimization library) for fitting statistical models via maximum-likelihood and least-squares methods, with built-in cost functions and Jupyter visualization.","skillfed_tags":["statistical-fitting","parameter-estimation","jupyter-friendly"],"use_cases":["Fit a probability density function to unbinned data and extract maximum-likelihood parameter estimates with error bars","Perform least-squares fitting to binned histograms with optional robustness to outliers","Combine multiple cost functions (e.g., signal + background models) and optimize jointly","Compute confidence intervals via Minos profile analysis for arbitrary confidence levels (with scipy)","Accelerate likelihood evaluation using Numba-compiled probability functions for high-dimensional data"],"what_it_does":"iminuit wraps MINUIT2, CERN's C++ optimization library, to solve parameter estimation problems in statistics and physics. It is designed for fitting statistical models\u2014finding best-fit parameters and computing error estimates via likelihood profile analysis. The package includes built-in cost functions for common tasks (unbinned and binned maximum-likelihood, least-squares, template fits with error propagation) and can combine multiple cost functions additively. It integrates with Jupyter for interactive fitting and visualization.\n\nThe package depends only on numpy at runtime but gains optional features from scipy (Minos intervals), numba (JIT compilation for speed), matplotlib (visualization), and ipywidgets (interactive mode). It supports current Python versions on macOS, Linux, and Windows, and has been actively maintained since 2012 with no known security vulnerabilities.","worth_installing":"Yes, if you need parameter estimation with rigorous error analysis. iminuit is production-stable, actively maintained, has no known vulnerabilities, and is the standard Python interface to MINUIT2. The dual MIT/LGPL license requires review for proprietary use. Medium install friction (compiled wheels) is typical for scientific packages and not a barrier on supported platforms."},"id":"iminuit","links":{"html":"https://skillfed.io/packages/iminuit","md":"https://skillfed.io/packages/iminuit.md","pypi":"https://pypi.org/project/iminuit/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2025-11-09","license_spdx":null,"license_treatment":"copyleft","name":"iminuit","python_support":"supports_current","summary":"Jupyter-friendly Python frontend for MINUIT2 in C++"},"popularity":{"monthly_downloads":226878,"position":9192,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2.32.0"}