Fast and user-friendly econometrics estimations: The R package fixest

TL;DR

fixest addresses the challenge of fast and flexible econometric estimation on high-dimensional fixed effects using a panel data formulation $y_{it} = \alpha_i + \beta_t + X_{it} \gamma + \epsilon_{it}$ and a novel fixed-point acceleration algorithm implemented in C++. It provides a unified, formula-based interface supporting OLS, IV (2SLS), GLM, and MLE, plus features for varying slopes and DiD, with on-the-fly VCOV adjustments and publication-ready output through etable and coefplot. The paper benchmarks fixest against leading open-source tools in R, Julia, and Python, showing best-in-class performance and scalable convergence across simple and difficult fixed-effects structures. It also demonstrates broad usability, including multiple estimations, interpolation, and panel-data helpers, making fixest valuable for teaching and software development. Overall, fixest delivers speed, robustness, and an integrated ecosystem that supports education, reproducible research, and development of econometric tools.

Abstract

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.