Generalized Linear Models (GLMs) with high-dimensional k-way fixed effects

Provides a routine to partial out factors with many levels during the optimization of the log-likelihood function of the corresponding GLM. The package is based on the algorithm described in Stammann (2018). It also offers an efficient algorithm to recover estimates of the fixed effects in a post-estimation routine and includes robust and multi-way clustered standard errors. Further the package provides analytical bias corrections for binary choice models derived by Fernández-Val and Weidner (2016) and Hinz, Stammann, and Wanner (2020). This package is a ground up rewrite with multiple refactors, optimizations, and new features compared to the original package alpaca. In its current state, the package is stable and future changes will be limited to bug fixes and improvements, but not to altering the functions' arguments or outputs.