Laurent R. Bergé, Kyle Butts, Grant McDermott
arXiv 29 Jan 2026 · Econometrics
arXiv:2601.21749 · PDF · DOI · OpenAlex · Extracted main text
fixest is an R package for fast and flexible econometric estimation, providing a comprehensive toolkit for applied researchers. The package particularly excels at fixed-effects estimation, supported by a novel fixed-point acceleration algorithm implemented in C++. This algorithm achieves rapid convergence across a broad class of data contexts and further enables estimation of complex models, including those with varying slopes, in a highly efficient manner. Beyond computational speed, fixest provides a unified syntax for a wide variety of models: ordinary least squares, instrumental variables, generalized linear models, maximum likelihood, and difference-in-differences estimators. An expressive formula interface enables multiple estimations, stepwise regressions, and variable interpolation in a single call, while users can make on-the-fly inference adjustments using a variety of built-in robust standard errors. Finally, fixest provides methods for publication-ready regression tables and coefficient plots. Benchmarks against leading alternatives in R, Python, and Julia demonstrate best-in-class performance, and the paper includes many worked examples illustrating the core functionality.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | The PyFixest Authors (2025) pyfixest: Fast high-dimensional fixed effect estimation in Python | 0.843 | 3 | 3 | 100% |
| 2 | Gaure, Simen (2013) lfe: Linear Group Fixed Effects | 0.843 | 3 | 3 | 100% |
| 3 | Gomez, Matthieu (2024) FixedEffectModels.Jl: Fast Estimation of Linear Models with IV and High Dimensional Categorical Variables | 0.843 | 3 | 3 | 100% |
| 4 | Stammann, Amrei (2018) Fast and Feasible Estimation of Generalized Linear Models with High-Dimensional k-Way Fixed Effects. | 0.843 | 3 | 3 | 100% |
| 5 | Johannes Boehm (2025) GLFixedEffectModels.jl | 0.644 | 2 | 2 | 100% |
| 6 | Conley, Timothy G (1999) GMM estimation with cross sectional dependence | 0.644 | 2 | 2 | 100% |
| 7 | Driscoll, John C and Kraay, Aart C (1998) Consistent covariance matrix estimation with spatially dependent panel data | 0.644 | 2 | 2 | 100% |
| 8 | Fong, David Chin-Lung and Saunders, Michael (2011) LSMR: An Iterative Algorithm for Sparse Least-Squares Problems | 0.644 | 2 | 2 | 100% |
| 9 | Frisch, Ragnar and Waugh, Frederick V (1933) Partial Time Regressions as Compared with Individual Trends | 0.644 | 2 | 2 | 100% |
| 10 | Gaure, Simen (2013) OLS with Multiple High Dimensional Category Variables | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 40 scored citations.