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
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-prerelease: lmboot_0.0.1.zip, r-release: lmboot_0.0.1.zip, r-oldrel: not available
macOS binaries: r-prerelease: lmboot_0.0.1.tgz, r-release: lmboot_0.0.1.tgz, r-oldrel: not available


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