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 an analytical bias-correction for binary choice models (logit and probit) derived by Fernandez-Val and Weidner (2016) <doi:10.1016/j.jeconom.2015.12.014>.
|Author||Amrei Stammann [aut, cre], Daniel Czarnowske [aut] (<https://orcid.org/0000-0002-0030-929X>)|
|Maintainer||Amrei Stammann <[email protected]>|
|Package repository||View on CRAN|
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