Fit GLM's with High-Dimensional k-Way Fixed Effects

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Documentation for package ‘alpaca’ version 0.3.4

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

alpaca-package	alpaca: A package for fitting glm's with high-dimensional k-way fixed effects
biasCorr	Asymptotic bias correction after fitting binary choice models with a one-/two-/three-way error component
coef.APEs	Extract estimates of average partial effects
coef.feglm	Extract estimates of structural parameters
coef.summary.APEs	Extract coefficient matrix for average partial effects
coef.summary.feglm	Extract coefficient matrix for structural parameters
feglm	Efficiently fit glm's with high-dimensional k-way fixed effects
feglm.control	Set 'feglm' Control Parameters
feglm.nb	Efficiently fit negative binomial glm's with high-dimensional k-way fixed effects
feglmControl	Set 'feglm' Control Parameters
fitted.feglm	Extract 'feglm' fitted values
getAPEs	Compute average partial effects after fitting binary choice models with a one-/two-/three-way error component
getFEs	Efficiently recover estimates of the fixed effects after fitting 'feglm'
predict.feglm	Predict method for 'feglm' fits
print.APEs	Print 'APEs'
print.feglm	Print 'feglm'
print.summary.APEs	Print 'summary.APEs'
print.summary.feglm	Print 'summary.feglm'
simGLM	Generate an artificial data set for some GLM's with two-way fixed effects
summary.APEs	Summarizing models of class 'APEs'
summary.feglm	Summarizing models of class 'feglm'
vcov.APEs	Compute covariance matrix after estimating 'APEs'
vcov.feglm	Compute covariance matrix after fitting 'feglm'