Fast Wild Cluster Bootstrap Inference for Linear Regression Models

Implementation of the fast algorithm for wild cluster bootstrap inference developed in Roodman et al (2019, STATA Journal) for linear regression models , which makes it feasible to quickly calculate bootstrap test statistics based on a large number of bootstrap draws even for large samples - as long as the number of bootstrapping clusters is not too large. Multiway clustering, regression weights, bootstrap weights, fixed effects and subcluster bootstrapping are supported. Further, both restricted (WCR) and unrestricted (WCU) bootstrap are supported. Methods are provided for a variety of fitted models, including 'lm()', 'feols()' (from package 'fixest') and 'felm()' (from package 'lfe').


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0.7 by Alexander Fischer, 20 days ago

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Authors: Alexander Fischer [aut, cre] , David Roodman [aut] , Achim Zeileis [ctb] (Author of included sandwich fragments) , Nathaniel Graham [ctb] (Contributor to included sandwich fragments) , Susanne Koell [ctb] (Contributor to included sandwich fragments) , Laurent Berge [ctb] (Author of included fixest fragments) , Sebastian Krantz [ctb]

Documentation:   PDF Manual  

GPL-3 license

Imports collapse, Formula, Rcpp, dreamerr, Matrix, Matrix.utils, generics, gtools, dqrng

Suggests fixest, lfe, data.table, fabricatr, tinytest, covr, knitr, rmarkdown, broom, modelsummary, RStata

Linking to Rcpp, RcppEigen

See at CRAN