alpaca 0.3.1
Fit GLM's with High-Dimensional k-Way Fixed Effects
Released May 24, 2019 by Amrei Stammann
This package cannot yet be used with Renjin it depends on other packages which are not available:
data.table 1.12.2
Dependencies
data.table 1.12.2
Formula 1.2-3
RcppArmadillo 0.9.500.2.0
MASS 7.3-51.4
Rcpp
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) 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) .
