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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "bstrap" in publications use:'
type: software
license: MIT
title: 'bstrap: Obtain and plot bootstrap sampling distributions of the sample mean'
version: 0.0.1
doi: 10.32614/CRAN.package.bstrap
identifiers:
- type: url
  value: https://nxskok.r-universe.dev/bstrap
abstract: 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.
authors:
- family-names: Butler
  given-names: Ken
  email: nxskok@gmail.com
repository: https://nxskok.r-universe.dev
commit: 22a06b6fb20d249b474c34a7e0563725222e37b7
url: https://worktree.ca/nxskok/bstrap
date-released: '2025-12-22'
contact:
- family-names: Butler
  given-names: Ken
  email: nxskok@gmail.com