Fit GLM's with High-Dimensional k-Way Fixed Effects
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) 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) and Hinz, Stammann, and Wanner (2020)
.