{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/6"}],"enrichment":{"capability":"QuantEcon provides computational tools and algorithms for quantitative economics research, including dynamic programming solvers, Markov chain analysis, and numerical methods built on NumPy, SciPy, and Numba.","skillfed_tags":["economics","dynamic-programming","numerical-methods"],"use_cases":["Solving Aiyagari-style heterogeneous-agent models using discrete dynamic programming","Analyzing Markov chains and computing stationary distributions for economic state transitions","Implementing numerical solutions to Bellman equations in macroeconomic models","Running quantitative economics simulations and computational experiments for research","Building reproducible economic models for academic papers and teaching materials"],"what_it_does":"QuantEcon is a scientific Python library for solving computational economics problems. It provides implementations of dynamic programming solvers (including discrete dynamic programming), Markov chain analysis, and other numerical methods commonly used in quantitative economics research. The library wraps and extends NumPy, SciPy, Numba, and SymPy to deliver high-performance algorithms suitable for academic and research workflows.\n\nThe package is designed for economists and researchers who need to solve dynamic optimization problems, analyze stochastic processes, or perform numerical simulations. It abstracts away low-level numerical details, allowing users to focus on model specification and interpretation. Installation is straightforward via pip or conda, and the library supports current Python versions.","worth_installing":"Yes, if you are doing quantitative economics research or teaching. The library is actively maintained, has no known vulnerabilities, installs with low friction, and provides well-tested algorithms for standard problems in computational economics. The unclear license status is a minor concern but unlikely to block use in academic or research settings; verify the MIT claim if compliance documentation is required."},"id":"quantecon","links":{"html":"https://skillfed.io/packages/quantecon","md":"https://skillfed.io/packages/quantecon.md","pypi":"https://pypi.org/project/quantecon/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-15","license_spdx":null,"license_treatment":"unclear","name":"quantecon","python_support":"supports_current","summary":"Import the main names to top level."},"popularity":{"monthly_downloads":168975,"position":10431,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.11.4"}