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 |
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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 [aut, cre], Rudolf Debelak [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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