{"categories":[{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/3"}],"enrichment":{"capability":"PyFixest performs fast high-dimensional fixed effects regression in Python, supporting OLS, WLS, IV, GLMs, quantile regression, and difference-in-differences estimation with multiple inference and post-estimation tools.","skillfed_tags":["econometrics","causal-inference","panel-data"],"use_cases":["Estimate causal effects in panel data using difference-in-differences or event study designs with multiple fixed effects.","Perform robust inference on high-dimensional models where standard OLS demeaning is computationally prohibitive.","Generate publication-ready regression tables with cluster-robust standard errors and multiple hypothesis corrections.","Run quantile regression with fixed effects to study heterogeneous treatment effects across the outcome distribution.","Conduct wild cluster bootstrap inference for valid inference under arbitrary cluster correlation structures."],"what_it_does":"PyFixest is a Python implementation of econometric fixed effects regression that closely mirrors the R package fixest's API and defaults. It handles estimation of linear models (OLS, WLS), instrumental variables (IV), generalized linear models (Poisson, logit, probit), and quantile regression in the presence of high-dimensional fixed effects\u2014a common problem in applied econometrics where naive approaches become computationally infeasible. The package offers multiple demeaning backends (MAP, within LSMR on CPU, and torch LSMR on GPU) to trade off speed and memory usage.\n\nBeyond estimation, PyFixest provides a full suite of econometric tools: robust and cluster-robust variance-covariance estimators, wild cluster bootstrap inference, multiple hypothesis corrections, randomization inference, and specialized estimators like the causal cluster variance estimator (CCV). It also supports difference-in-differences designs (TWFE, Did2s, local projections, Sun-Abraham event studies), regression decomposition, and publication-ready table generation via Great Tables or LaTeX. The 10 runtime dependencies (numpy, pandas, scipy, formulaic, joblib, narwhals, seaborn, tabulate, tqdm, maketables) provide the numerical, data manipulation, and visualization infrastructure.","worth_installing":"Yes, if you work in econometrics or causal inference with panel data or high-dimensional fixed effects. PyFixest is actively maintained, has no known vulnerabilities, and offers a mature feature set (OLS, IV, GLMs, DiD, quantile regression, robust inference) with GPU acceleration available. The MIT license is permissive. Install friction is moderate due to 10 dependencies, but wheels are widely available. Not worth installing if your regression problems are low-dimensional or you don't need the specialized econometric tools."},"id":"pyfixest","links":{"html":"https://skillfed.io/packages/pyfixest","md":"https://skillfed.io/packages/pyfixest.md","pypi":"https://pypi.org/project/pyfixest/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-11","license_spdx":null,"license_treatment":"permissive","name":"pyfixest","python_support":"supports_current","summary":"Fast high dimensional fixed effect estimation following syntax of the fixest R package."},"popularity":{"monthly_downloads":81153,"position":14245,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.60.0"}