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.

Last 30 days

This package has been downloaded 1,327 times in the last 30 days. Now we’re talking! This work is officially 'heard of in academic circles', just like those wild research papers on synthetic bananas. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 61 times.

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Last 365 days

This package has been downloaded 16,404 times in the last 365 days. The academic equivalent of having a dedicated subreddit. There are fans, and maybe even a few trolls! The day with the most downloads was Nov 05, 2024 with 121 downloads.

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