mlpwr: A Power Analysis Toolbox to Find Cost-Efficient Study Designs

We implement a surrogate modeling algorithm to guide simulation-based sample size planning. The method is described in detail in a recent preprint (Zimmer & Debelak (2022) <doi:10.31234/>). 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.

Version: 1.0.0
Imports: utils, stats, DiceKriging, digest, ggplot2, randtoolbox, rlist, WeightSVM, rgenoud
Suggests: knitr, lme4, lmerTest, mirt, pwr, rmarkdown, simr, sn, tidyr
Published: 2022-10-14
Author: Felix Zimmer ORCID iD [aut, cre], Rudolf Debelak ORCID iD [aut]
Maintainer: Felix Zimmer <felix.zimmer at>
License: GPL (≥ 3)
NeedsCompilation: no
Materials: README NEWS
CRAN checks: mlpwr results


Reference manual: mlpwr.pdf
Vignettes: simulation_functions


Package source: mlpwr_1.0.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): mlpwr_1.0.0.tgz, r-oldrel (arm64): mlpwr_1.0.0.tgz, r-release (x86_64): mlpwr_1.0.0.tgz, r-oldrel (x86_64): mlpwr_1.0.0.tgz


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