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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 |
Reference manual: | alpaca.pdf |
Vignettes: |
How to use alpaca Estimating the intensive and extensive margin of trade |
Package source: | alpaca_0.3.4.tar.gz |
Windows binaries: | r-devel: alpaca_0.3.4.zip, r-release: alpaca_0.3.4.zip, r-oldrel: alpaca_0.3.4.zip |
macOS binaries: | r-release (arm64): alpaca_0.3.4.tgz, r-oldrel (arm64): alpaca_0.3.4.tgz, r-release (x86_64): alpaca_0.3.4.tgz, r-oldrel (x86_64): alpaca_0.3.4.tgz |
Old sources: | alpaca archive |
Reverse suggests: | bife, lfe |
Reverse enhances: | texreg |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.