PowerUpR: Power Analysis Tools for Multilevel Randomized Experiments

Includes tools to calculate statistical power, minimum detectable effect size (MDES), MDES difference (MDESD), and minimum required sample size for various multilevel randomized experiments (MRE) with continuous outcomes. Accomodates 14 types of MRE designs to detect main treatment effect, seven types of MRE designs to detect moderated treatment effect (2-1-1, 2-1-2, 2-2-1, 2-2-2, 3-3-1, 3-3-2, and 3-3-3 designs; <total.lev> - <trt.lev> - <mod.lev>), five types of MRE designs to detect mediated treatment effects (2-1-1, 2-2-1, 3-1-1, 3-2-1, and 3-3-1 designs; <trt.lev> - <med.lev> - <out.lev>), four types of partially nested (PN) design to detect main treatment effect, and three types of PN designs to detect mediated treatment effects (2/1, 3/1, 3/2; <trt.arm.lev> / <ctrl.arm.lev>). See 'PowerUp!' Excel series at <https://www.causalevaluation.org/>.

Version: 1.1.0
Suggests: knitr, rmarkdown
Published: 2021-10-25
Author: Metin Bulus [aut, cre], Nianbo Dong [aut], Benjamin Kelcey [aut], Jessaca Spybrook [aut]
Maintainer: Metin Bulus <bulusmetin at gmail.com>
License: GPL (≥ 3)
NeedsCompilation: no
Citation: PowerUpR citation info
Materials: README NEWS
In views: ClinicalTrials
CRAN checks: PowerUpR results


Reference manual: PowerUpR.pdf
Vignettes: three-level cluster randomized trial


Package source: PowerUpR_1.1.0.tar.gz
Windows binaries: r-devel: PowerUpR_1.1.0.zip, r-release: PowerUpR_1.1.0.zip, r-oldrel: PowerUpR_1.1.0.zip
macOS binaries: r-release (arm64): PowerUpR_1.1.0.tgz, r-oldrel (arm64): PowerUpR_1.1.0.tgz, r-release (x86_64): PowerUpR_1.1.0.tgz, r-oldrel (x86_64): PowerUpR_1.1.0.tgz
Old sources: PowerUpR archive

Reverse dependencies:

Reverse suggests: PUMP


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