lmboot: Bootstrap in Linear Models

Various efficient and robust bootstrap methods are implemented for linear models with least squares estimation. Functions within this package allow users to create bootstrap sampling distributions for model parameters, test hypotheses about parameters, and visualize the bootstrap sampling or null distributions. Methods implemented for linear models include the wild bootstrap by Wu (1986) <doi:10.1214/aos/1176350142>, the residual and paired bootstraps by Efron (1979, ISBN:978-1-4612-4380-9), the delete-1 jackknife by Quenouille (1956) <doi:10.2307/2332914>, and the Bayesian bootstrap by Rubin (1981) <doi:10.1214/aos/1176345338>.

Version: 0.0.1
Depends: R (≥ 3.5.0)
Imports: evd (≥ 2.3.0), stats (≥ 3.6.0)
Published: 2019-06-03
DOI: 10.32614/CRAN.package.lmboot
Author: Megan Heyman [aut, cre]
Maintainer: Megan Heyman <heyman at rose-hulman.edu>
License: GPL-2
NeedsCompilation: no
CRAN checks: lmboot results


Reference manual: lmboot.pdf


Package source: lmboot_0.0.1.tar.gz
Windows binaries: r-devel: lmboot_0.0.1.zip, r-release: lmboot_0.0.1.zip, r-oldrel: lmboot_0.0.1.zip
macOS binaries: r-release (arm64): lmboot_0.0.1.tgz, r-oldrel (arm64): lmboot_0.0.1.tgz, r-release (x86_64): lmboot_0.0.1.tgz, r-oldrel (x86_64): lmboot_0.0.1.tgz

Reverse dependencies:

Reverse suggests: skedastic


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