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>.">

alpaca: Fit GLM's with High-Dimensional k-Way Fixed Effects (original) (raw)

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>.

Version: 0.3.5
Depends: R (≥ 3.1.0)
Imports: data.table, Formula, MASS, Rcpp, stats, utils
LinkingTo: Rcpp, RcppArmadillo
Suggests: bife, car, knitr, lfe, rmarkdown
Published: 2025-10-27
DOI: 10.32614/CRAN.package.alpaca
Author: Amrei Stammann ORCID iD [aut, cre], Daniel Czarnowske ORCID iD [aut]
Maintainer: Amrei Stammann <amrei.stammann at uni-bayreuth.de>
BugReports: https://github.com/amrei-stammann/alpaca/issues
License: GPL-3
URL: https://github.com/amrei-stammann/alpaca
NeedsCompilation: yes
Citation: alpaca citation info
Materials: README, NEWS
In views: CausalInference, Econometrics
CRAN checks: alpaca results

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