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: 0.3.4 Depends: R (≥ 3.1.0) Imports: data.table, Formula, MASS, Rcpp, stats, utils LinkingTo: Rcpp, RcppArmadillo Suggests: bife, car, knitr, lfe, rmarkdown Published: 2022-08-10 DOI: 10.32614/CRAN.package.alpaca Author: Amrei Stammann [aut, cre], Daniel Czarnowske [aut] Maintainer: Amrei Stammann <amrei.stammann at rub.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: NEWS In views: CausalInference, Econometrics CRAN checks: alpaca results Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=alpaca to link to this page.
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