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The {fwildclusterboot} package implements multiple fast wild cluster bootstrap algorithms as developed in Roodman et al (2019) and MacKinnon, Nielsen & Webb (2022).

Via the JuliaConnectoR, {fwildclusterboot} further ports functionality of WildBootTests.jl - which provides an even faster implementation of the wild cluster bootstrap for OLS and supports the WRE bootstrap for IV and tests of multiple joint hypotheses.

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:


You can install compiled versions of{fwildclusterboot} from CRAN (compiled), 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

The boottest() function

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


# set seed via dqset.seed for engine = "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")
#> Too guarantee reproducibility, don't forget to set a global random seed
#> **both** via `set.seed()` and `dqrng::dqset.seed()`.
#> This message is displayed once every 8 hours.
#> boottest.lm(object = lm_fit, param = "treatment", B = 9999, clustid = c("group_id1"))
#>  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   0.001    0.039     0.119


If you are in R, you can simply run the following command to get the BibTeX citation for {fwildclusterboot}:

#> To cite 'fwildclusterboot' in publications use:
#>   Fischer & Roodman. (2021). fwildclusterboot: Fast Wild Cluster
#>   Bootstrap Inference for Linear Regression Models. Available from
#>   https://cran.r-project.org/package=fwildclusterboot.
#> A BibTeX entry for LaTeX users is
#>   @Misc{,
#>     title = {fwildclusterboot: Fast Wild Cluster Bootstrap Inference for Linear Regression Models (Version},
#>     author = {Alexander Fischer and David Roodman},
#>     year = {2021},
#>     url = {https://cran.r-project.org/package=fwildclusterboot},
#>   }

Alternatively, if you prefer to cite the “Fast & Wild” paper by Roodman et al, it would be great if you mentioned {fwildclusterboot} in a footnote =) !