doi:10.1002/sim.7263>, is optimal for comparing paired or unpaired means in non-normal data, especially for small sample size studies. However, many researchers are unfamiliar with these methods. The 'bootwar' package bridges this gap by enabling users to grasp the concepts of nbpr via Boot War, a variation of the card game War designed for small samples. The package provides functions like score_keeper() and play_round() to streamline gameplay and scoring. Once a predetermined number of rounds concludes, users can employ the analyze_game() function to derive game results. This function leverages the 'npboottprm' package's nonparboot() to report nbpr results and, for comparative analysis, also reports results from the 'stats' package's t.test() function. Additionally, 'bootwar' features an interactive 'shiny' web application, bootwar(). This offers a user-centric interface to experience Boot War, enhancing understanding of nbpr methods across various distributions, sample sizes, number of bootstrap resamples, and confidence intervals.">

bootwar: Nonparametric Bootstrap Test with Pooled Resampling Card Game (original) (raw)

The card game War is simple in its rules but can be lengthy. In another domain, the nonparametric bootstrap test with pooled resampling (nbpr) methods, as outlined in Dwivedi, Mallawaarachchi, and Alvarado (2017) <doi:10.1002/sim.7263>, is optimal for comparing paired or unpaired means in non-normal data, especially for small sample size studies. However, many researchers are unfamiliar with these methods. The 'bootwar' package bridges this gap by enabling users to grasp the concepts of nbpr via Boot War, a variation of the card game War designed for small samples. The package provides functions like score_keeper() and play_round() to streamline gameplay and scoring. Once a predetermined number of rounds concludes, users can employ the analyze_game() function to derive game results. This function leverages the 'npboottprm' package's nonparboot() to report nbpr results and, for comparative analysis, also reports results from the 'stats' package's t.test() function. Additionally, 'bootwar' features an interactive 'shiny' web application, bootwar(). This offers a user-centric interface to experience Boot War, enhancing understanding of nbpr methods across various distributions, sample sizes, number of bootstrap resamples, and confidence intervals.

Version: 0.2.1
Depends: R (≥ 2.10)
Imports: ggplot2, mmcards, npboottprm, shiny, shinyjs, shinythemes
Suggests: knitr, rmarkdown, testthat (≥ 3.0.0)
Published: 2023-10-01
DOI: 10.32614/CRAN.package.bootwar
Author: Mackson Ncube [aut, cre], mightymetrika, LLC [cph, fnd]
Maintainer: Mackson Ncube <macksonncube.stats at gmail.com>
BugReports: https://github.com/mightymetrika/bootwar/issues
License: MIT + file
URL: https://github.com/mightymetrika/bootwar
NeedsCompilation: no
Materials: README NEWS
CRAN checks: bootwar results

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