Implementation of the fast algorithm for wild cluster bootstrap inference developed in Roodman et al (2019, STATA Journal) for linear regression models <https://journals.sagepub.com/doi/full/10.1177/1536867X19830877>, 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').
Package details 


Author  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] 
Maintainer  Alexander Fischer <alexanderfischer1801@tonline.de> 
License  GPL3 
Version  0.3.5 
URL  https://s3alfisc.github.io/fwildclusterboot/ 
Package repository  View on CRAN 
Installation 
Install the latest version of this package by entering the following in R:

Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.