DESCRIPTION

Package: fwildclusterboot
Title: Fast Wild Cluster Bootstrap Inference for Linear Models
Version: 0.14.3
Authors@R: c(
    person("Alexander", "Fischer", , "alexander-fischer1801@t-online.de", role = c("aut", "cre")),
    person("David", "Roodman", role = "aut"),
    person("Megha", "Joshi", 
            role = "rev", 
            comment = "Megha reviewed the package (v. 0.13) for ropensci 
                      , see <https://github.com/ropensci/software-review/issues/546>"
          ),
    person("Eunseop", "Kim",
            role = "rev", 
            comment = "Eunseop reviewed the package (v. 0.13) for ropensci 
                      , see <https://github.com/ropensci/software-review/issues/546>"
          ),
    person("Achim", "Zeileis", role = "ctb",
           comment = "Author of included sandwich fragments"),
    person("Nathaniel", "Graham", role = "ctb",
           comment = "Contributor to included sandwich fragments"),
    person("Susanne", "Koell", role = "ctb",
           comment = "Contributor to included sandwich fragments"),
    person("Laurent", "Berge", role = "ctb",
           comment = "Author of included fixest fragments"),
    person("Sebastian", "Krantz", role = "ctb")
  )
Description: Implementation of fast algorithms for wild cluster bootstrap
    inference developed in 'Roodman et al' (2019, 'STATA' Journal,
    <doi:10.1177/1536867X19830877>) and 'MacKinnon et al' (2022), 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, Nielsen & Webb' (2022) are
    supported.  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 bootstrap.  Last, 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') and hypotheses with q > 1.
License: GPL-3
URL: https://s3alfisc.github.io/fwildclusterboot/
BugReports: https://github.com/s3alfisc/fwildclusterboot/issues/
Imports:
    collapse,
    dqrng,
    dreamerr,
    Formula,
    generics,
    gtools,
    JuliaConnectoR,
    Matrix,
    Rcpp,
    rlang,
    summclust
Suggests:
    bench,
    broom,
    clubSandwich,
    covr,
    data.table,
    fabricatr,
    fixest,
    gt,
    ivreg,
    knitr,
    lfe,
    lmtest,
    modelsummary,
    rmarkdown,
    sandwich,
    testthat (>= 3.0.0),
    tibble, 
    MASS
LinkingTo:
    Rcpp,
    RcppArmadillo,
    RcppEigen
VignetteBuilder:
    knitr
Config/testthat/edition: 3
Encoding: UTF-8
Language: en-US
LazyData: true
Roxygen: list(markdown = TRUE, roclets = c ("namespace", "rd",
    "srr::srr_stats_roclet"))
RoxygenNote: 7.2.3
SystemRequirements: Version Requirements to run the wild bootstrap through
    Julia - Julia (>= 1.8), WildBootTests.jl (>=0.9.8).  Julia is
    downloadable via the official Julia website
    (https://julialang.org/downloads/), WildBootTests.jl via Julia's
    package manager (https://docs.julialang.org/en/v1/stdlib/Pkg/) or its
    github repository (https://github.com/droodman/WildBootTests.jl)