quantecon
Import the main names to top level.
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low friction installation with a pure-wheel distribution.
- Active maintenance with a release 30 days ago and ongoing repository activity.
- Depends on well-established scientific libraries (NumPy, SciPy, Numba, SymPy, requests), all standard in scientific Python environments.
License · maintenance · safety
(unclear) — License treatment is unclear in the package metadata; the description excerpt mentions MIT licensing, but the fact sheet does not confirm this formally. Verify the actual license before relying on it for compliance purposes.
last release 2026-07-15 (30 days) · last repo commit 2026-08-14 · 2,388 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 168,975 downloads/mo, #10,431 on PyPI
Alternatives
Verify before relying
pip install quantecon
import quantecon as qe
from quantecon.markov import DiscreteDP
R, Q, beta = ... # define reward, transition, discount
aiyagari_ddp = DiscreteDP(R, Q, beta)
results = aiyagari_ddp.solve(method='policy_iteration')- Whether the MIT license mentioned in the description excerpt is formally declared in package metadata and what restrictions or obligations it imposes
- Performance characteristics and scalability limits for large-scale economic models
- Availability and completeness of documentation for all solver methods beyond policy iteration
What it is and 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.
The 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.
Use it for
- 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
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
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.
Install
quantecon on PyPI
Before you install
Low friction installation with a pure-wheel distribution. Active maintenance with a release 30 days ago and ongoing repository activity. Depends on well-established scientific libraries (NumPy, SciPy, Numba, SymPy, requests), all standard in scientific Python environments.
License in practice
License treatment is unclear in the package metadata; the description excerpt mentions MIT licensing, but the fact sheet does not confirm this formally. Verify the actual license before relying on it for compliance purposes.
Quickstart
pip install quantecon
import quantecon as qe
from quantecon.markov import DiscreteDP
R, Q, beta = ... # define reward, transition, discount
aiyagari_ddp = DiscreteDP(R, Q, beta)
results = aiyagari_ddp.solve(method='policy_iteration')Verify before relying
- Whether the MIT license mentioned in the description excerpt is formally declared in package metadata and what restrictions or obligations it imposes
- Performance characteristics and scalability limits for large-scale economic models
- Availability and completeness of documentation for all solver methods beyond policy iteration
Package facts
| License | Not declared unclear |
| Python support | Supports the current Python release >=3.7 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagesnumbanumpyrequestsscipysympy |
| Maintenance | Actively maintained 30 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 168,975 / month, #10,431 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: Science/ResearchOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/Engineering |
Evidence: quantecon-0.11.4-py3-none-any.whl
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