Package: bstrap
Title: Obtain and plot bootstrap sampling distributions of the sample
        mean
Version: 0.0.1
Authors@R: 
    person("Ken", "Butler", , "nxskok@gmail.com", role = c("aut", "cre"))
Description: Many common tests assume sufficient normality of the data
        distribution, with the received wisdom that if the sample size
        is "large", the normality doesn't matter so much (because of
        the Central Limit Theorem). It is difficult to judge the
        normality is good enough, or whether the sample size is big
        enough. A better way to investigate is to obtain a bootstrap
        sampling distribution of the sample mean (by taking repeated
        bootstrap samples), and to assess that distribution for
        normality. If it is, the normal-theory test will work; if not,
        not.
License: MIT + file LICENSE
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.3.3
URL: https://worktree.ca/nxskok/bstrap
        https://nxskok.r-universe.dev/bstrap
Imports: dplyr, ggplot2, magrittr, tibble, tidyr
Suggests: testthat (>= 3.0.0)
Config/testthat/edition: 3
Depends: R (>= 3.5)
LazyData: true
Config/pak/sysreqs: libicu-dev
Repository: https://nxskok.r-universe.dev
Date/Publication: 2025-12-22 22:15:22 UTC
RemoteUrl: https://worktree.ca/nxskok/bstrap
RemoteRef: HEAD
RemoteSha: 22a06b6fb20d249b474c34a7e0563725222e37b7
NeedsCompilation: no
Packaged: 2026-07-23 05:56:29 UTC; root
Author: Ken Butler [aut, cre]
Maintainer: Ken Butler <nxskok@gmail.com>