c(
person("Amrei", "Stammann", email = "amrei.stammann@hhu.de", role = c("aut", "cre")),
person("Daniel", "Czarnowske", email = "daniel.czarnowske@hhu.de", role = c("aut"),
comment = c(ORCID = "0000-0002-0030-929X")))
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>.
Amrei Stammann [aut, cre],
Daniel Czarnowske [aut] (<https://orcid.org/0000-0002-0030-929X>)

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).

c(
person("Amrei", "Stammann", email = "amrei.stammann@rub.de", role = c("aut", "cre")),
person("Daniel", "Czarnowske", email = "daniel.czarnowske@hhu.de", role = c("aut"),
comment = c(ORCID = "0000-0002-0030-929X")))
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) <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 (2020)
<arXiv:2004.12655>.