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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/osf.io/tnhb2>). 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: | 1.1.0 |
Depends: | R (≥ 2.10) |
Imports: | utils, stats, DiceKriging, digest, ggplot2, randtoolbox, rlist, WeightSVM, rgenoud |
Suggests: | knitr, lme4, lmerTest, mirt, pwr, rmarkdown, simr, sn, tidyr |
Published: | 2023-08-07 |
DOI: | 10.32614/CRAN.package.mlpwr |
Author: | Felix Zimmer |
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 |
Materials: | README NEWS |
CRAN checks: | mlpwr results |
Reference manual: | mlpwr.pdf |
Vignettes: |
extensions simulation_functions |
Package source: | mlpwr_1.1.0.tar.gz |
Windows binaries: | r-devel: mlpwr_1.1.0.zip, r-release: mlpwr_1.1.0.zip, r-oldrel: mlpwr_1.1.0.zip |
macOS binaries: | r-release (arm64): mlpwr_1.1.0.tgz, r-oldrel (arm64): mlpwr_1.1.0.tgz, r-release (x86_64): mlpwr_1.1.0.tgz, r-oldrel (x86_64): mlpwr_1.1.0.tgz |
Old sources: | mlpwr archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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