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The fwildclusterboot package provides a native R implementation of the fast wild cluster bootstrap algorithm developed in Roodman et al (2019) for regression objects in R.

It also ports functionality of WildBootTests.jl to R via the JuliaConnectoR.

The package’s central function is boottest(). It allows to test univariate hypotheses using a wild cluster bootstrap at extreme speed: via the ‘fast’ algorithm, it is possible to run a wild cluster bootstrap with B = 100.000 iterations in less than a second!

fwildclusterboot supports the following features:

Additional features are provided through WildBootTests.jl:

fwildclusterboot supports the following models:

The boottest() function

For a longer introduction to fwildclusterboot, take a look at the vignette.


# set seed via dqset.seed for boot_algo = "R" & Rademacher, Webb & Normal weights
# set 'familiar' seed for all other algorithms and weight types 


# fit the model via fixest::feols(), lfe::felm() or stats::lm()
lm_fit <- lm(proposition_vote ~ treatment  + log_income + as.factor(Q1_immigration) + as.factor(Q2_defense), data = voters)
# bootstrap inference via boottest()
lm_boot <- boottest(lm_fit, clustid = c("group_id1"), B = 9999, param = "treatment", seed = 1)
#> boottest.lm(object = lm_fit, param = "treatment", B = 9999, clustid = c("group_id1"), 
#>     seed = 1)
#>  Hypothesis: 1*treatment = 0
#>  Observations: 300
#>   Bootstr. Type: rademacher
#>  Clustering: 1-way
#>  Confidence Sets: 95%
#>  Number of Clusters: 40
#>              term estimate statistic p.value conf.low conf.high
#> 1 1*treatment = 0    0.079     3.983       0     0.04     0.118


You can install compiled versions offwildclusterboot from CRAN and the development version from R-universe (compiled) or github by following one of the steps below:

# from CRAN 
# from r-universe (windows & mac, compiled R > 4.0 required)
install.packages('fwildclusterboot', repos ='https://s3alfisc.r-universe.dev')
# dev version from github
# note: installation requires Rtools

To run WildBootTests.jl through fwildclusterboot, Julia and WildBootTests.jl need to be installed.

You can install Julia by following the steps described on the official Julia homepage: https://julialang.org/downloads/. WildBootTests.jl can then be installed via Julia’s package management system.

To install WildBootTests.jl and Julia from within R, you can alternatively use the JuliaConnectoR.utils package.

install_julia() # install Julia
connect_julia_r() # instructions to connect Julia and R
install_julia_packages("WildBootTests.jl") # install WildBootTests.jl
set_julia_ntreads() # instructions to set nthreads for Julia