Title: Fast Wild Cluster Bootstrap Inference for Linear Models
comment = "Author of included sandwich fragments"),
comment = "Contributor to included sandwich fragments"),
comment = "Author of included fixest fragments"),
Description: Implementation of fast algorithms for wild cluster bootstrap
inference developed in 'Roodman et al' (2019, 'STATA' Journal,
which makes it feasible to quickly calculate bootstrap test
statistics based on a large number of bootstrap draws even for
large samples. Multiple bootstrap types as described in 'MacKinnon,
Further, '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').
Additionally implements a 'heteroskedasticity-robust' ('HC1') wild
Further, the package provides an R binding to 'WildBootTests.jl',
which provides additional speed gains and functionality,
including the 'WRE' bootstrap for instrumental variable models
(based on models of type 'ivreg()' from package 'ivreg')
Imports: collapse, dreamerr, Formula, generics, dqrng, gtools, Matrix,
Suggests: fixest, lfe, ivreg, clubSandwich, lmtest, data.table,
fabricatr, covr, knitr, rmarkdown, broom, modelsummary, bench,
SystemRequirements: Julia (>= 1.7), WildBootTests.jl (>=0.8.1)
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),

Title: Fast Wild Cluster Bootstrap Inference for Linear Regression
Description: Implementation of the fast algorithm for wild cluster bootstrap
inference developed in Roodman et al (2019, STATA Journal) 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
Imports: collapse, Formula, Rcpp, dreamerr, Matrix, Matrix.utils,
Suggests: fixest, lfe, data.table, fabricatr, tinytest, covr, knitr,