get_seed

<p>creates an integer based on the global random seed set via set.seed()
for using set.seed() for controlling rcpp's seed, see this
blog post http://rorynolan.rbind.io/2018/09/30/rcsetseed/</p>

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.

Alexander Fischer

fwildclusterboot

Fast Wild Cluster Bootstrap Inference for Linear Models

David Roodman

Achim Zeileis

Nathaniel Graham

Susanne Koell

Laurent Berge

Sebastian Krantz

get_seed function

creates an integer based on the global random seed set via set.seed()
for using set.seed() for controlling rcpp's seed, see this
blog post http://rorynolan.rbind.io/2018/09/30/rcsetseed/ — get_seed

creates an integer based on the global random seed set via set.seed()
for using set.seed() for controlling rcpp's seed, see this
blog post http://rorynolan.rbind.io/2018/09/30/rcsetseed/

get_seed: creates an integer based on the global random seed set via set.seed() for using set.seed() for controlling rcpp's seed, see this blog post http://rorynolan.rbind.io/2018/09/30/rcsetseed/

Description

Usage

Arguments