---
id: pyhdfe
version: "0.2.0"
license: MIT
license_treatment: permissive
maintenance: dormant
---

# pyhdfe — High dimensional fixed effect absorption with Python 3

License: permissive · Maintenance: dormant · Downloads: 498.9K/mo

## 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 above — verify before relying.

PyHDFE absorbs high-dimensional fixed effects from regression data using optimized algorithms, designed for integration into statistical projects rather than as a standalone regression interface.

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

pip install pyhdfe
uv add pyhdfe
poetry add pyhdfe

## Installing pyhdfe

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.

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 data

Requires Python 3.6 or later; numpy and scipy must be installed.

Verify 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_current
- Install friction: low
- Maintenance: dormant
- Downloads: 498.9K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

fixed effects absorption, high dimensional fixed effects, regression fixed effects, fixed effect algorithms, statistical fixed effects python, absorb fixed effects, hdfe regression, econometrics, fixed-effects, regression-tools

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