Package: AOboot 0.1.2

Christian Blötner
AOboot: Bootstrapping in Different One-Way and Two-Way ANOVA
To address the violation of the assumption of normally distributed variables, researchers frequently employ bootstrapping. Building upon established packages for R (Sigmann et al. (2024) <doi:10.32614/CRAN.package.afex>, Lenth (2024) <doi:10.32614/CRAN.package.emmeans>), we provide bootstrapping functions to approximate a normal distribution of the parameter estimates for between-subject, within-subject, and mixed one-way and two-way ANOVA.
Authors:
AOboot_0.1.2.tar.gz
AOboot_0.1.2.tar.gz(r-4.7-any)AOboot_0.1.2.tar.gz(r-4.6-any)
AOboot_0.1.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
AOboot/json (API)
| # Install 'AOboot' in R: |
| install.packages('AOboot', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:f54657495f. Checks:4 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 156 | ||
| source / vignettes | OK | 207 | ||
| linux-release-x86_64 | OK | 301 | ||
| wasm-release | OK | 117 |
Exports:AOboot_oneAOboot_twoAObootBetweenAObootMixedAObootWithinprint.AOboot_oneprint.AOboot_two
Dependencies:abindafexbackportsbootbroomcarcarDataclicolorspacecowplotcpp11DerivdoBydplyremmeansestimabilityfarverforecastFormulafracdiffgenericsggplot2gluegtableisobandlabelinglatticelifecyclelme4lmerTestlmtestlsrmagrittrMASSMatrixMatrixModelsmgcvmicrobenchmarkminqamodelrmvtnormnlmenloptrnnetnumDerivpbkrtestpillarpkgconfigplyrpurrrquantregR6rbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreformulasreshape2rlangS7scalesSparseMstringistringrsurvivaltibbletidyrtidyselecttimeDateurcautf8vctrsviridisLitewithrzoo
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| AOboot.one Class | AOboot_one print.AOboot_one |
| AOboot.two Class | AOboot_two print.AOboot_two |
| Bootstrapped ANOVA for Between-Subject Designs | AObootBetween |
| Bootstrapped ANOVA for Mixed Designs | AObootMixed |
| Bootstrapped ANOVA for Within-Subject Designs | AObootWithin |