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fwildclusterboot  

Fast Wild Cluster Bootstrap Inference for Linear Models
View on CRAN: Click here


Download and install fwildclusterboot package within the R console
Install from CRAN:
install.packages("fwildclusterboot")

Install from Github:
library("remotes")
install_github("cran/fwildclusterboot")

Install by package version:
library("remotes")
install_version("fwildclusterboot", "0.13.0")



Attach the package and use:
library("fwildclusterboot")
Maintained by
Alexander Fischer
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2021-02-16
Latest Update: 2023-02-26
Description:
Implementation of fast algorithms for wild cluster bootstrap inference developed in 'Roodman et al' (2019, 'STATA' Journal, ) 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.
How to cite:
Alexander Fischer (2021). fwildclusterboot: Fast Wild Cluster Bootstrap Inference for Linear Models. R package version 0.13.0, https://cran.r-project.org/web/packages/fwildclusterboot
Previous versions and publish date:
0.3.1 (2021-02-16 10:10), 0.3.2 (2021-02-26 16:10), 0.3.3 (2021-04-12 19:10), 0.3.4 (2021-05-01 19:20), 0.3.5 (2021-06-20 16:00), 0.3.6 (2021-08-02 01:10), 0.3.7 (2021-09-14 13:10), 0.4 (2021-10-08 10:30), 0.5.1 (2021-11-07 01:20), 0.5 (2021-11-03 19:20), 0.7 (2022-01-03 20:00), 0.8 (2022-04-19 02:04), 0.9 (2022-06-10 23:20), 0.12.1 (2023-01-23 15:00), 0.12 (2022-10-16 01:02)
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