alpaca

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.

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