Fit GLM's with High-Dimensional k-Way Fixed Effects R package alpaca version 0.3.5 CRAN Task View: CausalInference (https://CRAN.R-project.org/view=CausalInference) CRAN Task View: Econometrics (https://CRAN.R-project.org/view=Econometrics) Provides a routine to partial out factors with many levels during the optimization of the log-likelihood function of the corresponding generalized linear model (glm). The package is based on the algorithm described in Stammann (2018) <doi:10.48550/arXiv.1707.01815> and is restricted to glm's that are based on maximum likelihood estimation and nonlinear. 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 Fernandez-Val and Weidner (2016) <doi:10.1016/j.jeconom.2015.12.014> and Hinz, Stammann, and Wanner (2020) <doi:10.48550/arXiv.2004.12655>. Software Depends: R (>= 3.1.0) Imports: data.table, Formula, MASS, Rcpp, stats, utils LinkingTo: Rcpp, RcppArmadillo Suggests: bife, car, knitr, lfe, rmarkdown Amrei Stammann <amrei.stammann@uni-bayreuth.de> Comprehensive R Archive Network (CRAN) Amrei Stammann [aut, cre] (ORCID: <https://orcid.org/0000-0003-1136-286X>), Daniel Czarnowske [aut] (ORCID: <https://orcid.org/0000-0002-0030-929X>) GPL-3 2025-10-27 application/tgz https://CRAN.R-project.org/package=alpaca doi:10.32614/CRAN.package.alpaca