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/>.

Package details

AuthorMetin Bulus [aut, cre], Nianbo Dong [aut], Benjamin Kelcey [aut], Jessaca Spybrook [aut]
MaintainerMetin Bulus <bulusmetin@gmail.com>
LicenseGPL (>= 3)
Version1.1.0
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("PowerUpR")

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PowerUpR documentation built on Oct. 25, 2021, 5:06 p.m.