doi:10.1037/met0000611>). It supports multiple study design parameters and optimization with respect to a cost function. It can find optimal designs that correspond to a desired statistical power or that fulfill a cost constraint. We also provide a tutorial paper (Zimmer et al. (2023) <doi:10.3758/s13428-023-02269-0>).">

mlpwr: A Power Analysis Toolbox to Find Cost-Efficient Study Designs (original) (raw)

We implement a surrogate modeling algorithm to guide simulation-based sample size planning. The method is described in detail in our paper (Zimmer & Debelak (2023) <doi:10.1037/met0000611>). It supports multiple study design parameters and optimization with respect to a cost function. It can find optimal designs that correspond to a desired statistical power or that fulfill a cost constraint. We also provide a tutorial paper (Zimmer et al. (2023) <doi:10.3758/s13428-023-02269-0>).

Version: 1.1.1
Depends: R (≥ 3.5.0)
Imports: utils, stats, DiceKriging, digest, ggplot2, randtoolbox, rlist, rgenoud
Suggests: knitr, lme4, lmerTest, mirt, pwr, rmarkdown, simr, sn, tidyr, WeightSVM
Published: 2024-10-03
DOI: 10.32614/CRAN.package.mlpwr
Author: Felix Zimmer ORCID iD [aut, cre], Rudolf Debelak ORCID iD [aut], Marc Egli [ctb]
Maintainer: Felix Zimmer <felix.zimmer at mail.de>
BugReports: https://github.com/flxzimmer/mlpwr/issues
License: GPL (≥ 3)
URL: https://github.com/flxzimmer/mlpwr
NeedsCompilation: no
Citation: mlpwr citation info
Materials: README NEWS
CRAN checks: mlpwr results

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