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: |
1.0.0 |
| 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 
[aut, cre],
Rudolf Debelak
[aut] |
| 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 |