pyhdfe
High dimensional fixed effect absorption with Python 3
Decision gist · record as of 2026-08-14
Yes, if you need to absorb fixed effects as a component of a statistical project and are comfortable integrating it into your own code rather than using a turnkey regression interface. The low install friction, permissive license, and stable dependencies make it straightforward to add. The dormant maintenance is not a blocker for a mature algorithm library, but verify that the specific algorithms and convergence criteria meet your research needs.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.6 or later; numpy and scipy must be installed.
- Low friction: pure Python wheel with only numpy and scipy as dependencies.
- Dormant maintenance—last release 1092 days ago with a commit in February 2024—but the repository remains active and unarchived, suggesting stable rather than abandoned status.
License · maintenance · safety
MIT (permissive) — MIT license is permissive, allowing commercial and private use with minimal restrictions. You may use, modify, and distribute the package freely as long as you include the license notice.
last release 2023-08-18 (1092 days) · last repo commit 2024-02-07 · 60 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 498,890 downloads/mo, #6,326 on PyPI
Alternatives
Verify before relying
pip install pyhdfe
import pyhdfe
# Use pyhdfe algorithms to absorb fixed effects in your regression data- Specific algorithm implementations and convergence criteria supported by the package.
- Performance benchmarks or scaling characteristics for different problem sizes.
- Whether the package is actively maintained or in stable maintenance mode.
What it is and what it does
PyHDFE is a Python library for absorbing high-dimensional fixed effects in regression problems. It implements algorithms designed to be incorporated into statistical workflows rather than provide a complete regression interface—the goal is to offer performant fixed-effect absorption that researchers can embed in their own projects.
The package depends on numpy and scipy, installs as a pure Python wheel with low friction, and is tested on Python versions 3.6 through 3.9. It was created to facilitate fair comparison of fixed-effects algorithms previously implemented in various languages and to support statistical projects that need efficient fixed-effect handling as a component, not a standalone tool.
Use it for
- Absorb fixed effects in large econometric datasets before fitting other models.
- Compare fixed-effects absorption algorithms across different implementations and convergence criteria.
- Integrate fixed-effect absorption into custom statistical pipelines or research code.
- Handle high-dimensional categorical variables in regression preprocessing workflows.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need to absorb fixed effects as a component of a statistical project and are comfortable integrating it into your own code rather than using a turnkey regression interface.
The low install friction, permissive license, and stable dependencies make it straightforward to add. The dormant maintenance is not a blocker for a mature algorithm library, but verify that the specific algorithms and convergence criteria meet your research needs.
Install
pyhdfe on PyPI
Before you install
Low friction: pure Python wheel with only numpy and scipy as dependencies. Dormant maintenance—last release 1092 days ago with a commit in February 2024—but the repository remains active and unarchived, suggesting stable rather than abandoned status.
Requires Python 3.6 or later; numpy and scipy must be installed.
License in practice
MIT license is permissive, allowing commercial and private use with minimal restrictions. You may use, modify, and distribute the package freely as long as you include the license notice.
Quickstart
pip install pyhdfe
import pyhdfe
# Use pyhdfe algorithms to absorb fixed effects in your regression dataVerify before relying
- Specific algorithm implementations and convergence criteria supported by the package.
- Performance benchmarks or scaling characteristics for different problem sizes.
- Whether the package is actively maintained or in stable maintenance mode.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.6 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagesnumpyscipy |
| Maintenance | Dormant 1,092 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 498,890 / month, #6,326 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/ResearchLicense :: OSI Approved :: MIT LicenseOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.6Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Scientific/Engineering |
Evidence: pyhdfe-0.2.0-py3-none-any.whl
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