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 [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 resultsRetroSearch is an open source project built by @garambo | Open a GitHub Issue
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