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


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Documentation for package ‘alpaca’ version 0.3.3

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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 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 of average partial effects
coef.summary.feglm Extract coefficient matrix of 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 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.feglm Extract estimates of the covariance matrix