Package: fwildclusterboot
Title: Fast Wild Cluster Bootstrap Inference for Linear Models
Version: 0.8
Date: 2022-04-18
Authors@R: 
    c(
    person(given = "Alexander",
           family = "Fischer",
           role = c("aut", "cre"),
           email = "alexander-fischer1801@t-online.de"),
    person(given = "David", 
           family = "Roodman", 
           role = "aut"), 
    person(given = "Achim", 
           family = "Zeileis",
           role = c("ctb"),
           comment = "Author of included sandwich fragments"),
	person(given = "Nathaniel",
	       family = "Graham",
	       role = c("ctb"), 
           comment = "Contributor to included sandwich fragments"),
	person(given = "Susanne",
	       family = "Koell", 
	       role = c("ctb"), 
           comment = "Contributor to included sandwich fragments"),
	person("Laurent", 
	        "Berge",
	        role = c("ctb"), 
            comment = "Author of included fixest fragments"),
	 person("Sebastian", 
	        "Krantz", 
	         role = c("ctb"))
	)
Description: Implementation of the fast algorithm for wild cluster bootstrap 
             inference developed in Roodman et al (2019, STATA Journal) for 
             linear regression models <doi: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. 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.
             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')
             and hypotheses with q > 1.
URL: https://s3alfisc.github.io/fwildclusterboot/
BugReports: https://github.com/s3alfisc/fwildclusterboot/issues/
License: GPL-3
Imports: collapse, Formula, Rcpp, dreamerr, Matrix, Matrix.utils,
        generics, gtools, dqrng, JuliaConnectoR
Suggests: fixest, lfe, ivreg, clubSandwich, sandwich, lmtest,
        data.table, fabricatr, covr, knitr, rmarkdown, broom,
        modelsummary, bench, testthat (>= 3.0.0)
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.2
LinkingTo: Rcpp,RcppArmadillo, RcppEigen
VignetteBuilder: knitr
Language: en-US
SystemRequirements: Julia (>= 1.7), WildBootTests.jl (>=0.7.13)
Config/testthat/edition: 3
NeedsCompilation: yes
Packaged: 2022-04-18 15:01:31 UTC; alexa
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 <alexander-fischer1801@t-online.de>
Repository: CRAN
Date/Publication: 2022-04-19 00:04:29 UTC
Built: R 4.0.5; x86_64-w64-mingw32; 2022-04-20 16:31:17 UTC; windows
Archs: i386, x64
