alpaca: Fit GLM's 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 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 |
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