alpaca: Fit GLM's with High-Dimensional k-Way Fixed Effects

Provides a routine to concentrate 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 proposed by Stammann (2018) <arXiv:1707.01815> and is restricted to glm's that are based on maximum likelihood estimation and non-linear. 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 (logit and probit) derived by Fernandez-Val and Weidner (2016) <doi:10.1016/j.jeconom.2015.12.014> and Hinz, Stammann, and Wanner (2019).

Version: 0.3.2
Depends: R (≥ 3.1.0)
Imports: data.table, Formula, MASS, Rcpp, stats, utils
LinkingTo: Rcpp, RcppArmadillo
Suggests: bife, car, knitr, lfe
Published: 2020-01-12
Author: Amrei Stammann [aut, cre], Daniel Czarnowske ORCID iD [aut]
Maintainer: Amrei Stammann <amrei.stammann at>
License: GPL-3
NeedsCompilation: yes
Citation: alpaca citation info
Materials: NEWS
In views: Econometrics
CRAN checks: alpaca results


Reference manual: alpaca.pdf
Vignettes: How to use alpaca
Estimating the intensive and extensive margin of trade
Package source: alpaca_0.3.2.tar.gz
Windows binaries: r-devel:, r-devel-gcc8:, r-release:, r-oldrel:
OS X binaries: r-release: alpaca_0.3.2.tgz, r-oldrel: alpaca_0.3.2.tgz
Old sources: alpaca archive

Reverse dependencies:

Reverse suggests: bife, lfe


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