Sample sizes are often small due to hard to reach target populations, rare target events, time constraints, limited budgets, or ethical considerations. Two statistical methods with promising performance in small samples are the nonparametric bootstrap test with pooled resampling method, which is the focus of Dwivedi, Mallawaarachchi, and Alvarado (2017) <doi:10.1002/sim.7263>, and informative hypothesis testing, which is implemented in the 'restriktor' package. The 'npboottprmFBar' package uses the nonparametric bootstrap test with pooled resampling method to implement informative hypothesis testing. The bootFbar() function can be used to analyze data with this method and the persimon() function can be used to conduct performance simulations on type-one error and statistical power.
Version: 0.2.0 Imports: DT, fGarch, lmPerm, mmints, npboottprm, restriktor, shiny Suggests: testthat (≥ 3.0.0) Published: 2024-09-08 DOI: 10.32614/CRAN.package.npboottprmFBar Author: Mackson Ncube [aut, cre], mightymetrika, LLC [cph, fnd] Maintainer: Mackson Ncube <macksonncube.stats at gmail.com> BugReports: https://github.com/mightymetrika/npboottprmFBar/issues License: MIT + file LICENSE URL: https://github.com/mightymetrika/npboottprmFBar NeedsCompilation: no Materials: README CRAN checks: npboottprmFBar results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=npboottprmFBar to link to this page.
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