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 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>).
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.1.1 |
rolling linux/jammy R-4.5 | mlpwr_1.1.1.tar.gz |
1.2 MiB |
1.1.1 |
rolling linux/noble R-4.5 | mlpwr_1.1.1.tar.gz |
1.2 MiB |
1.1.1 |
rolling source/ R- | mlpwr_1.1.1.tar.gz |
963.0 KiB |
1.1.1 |
latest linux/jammy R-4.5 | mlpwr_1.1.1.tar.gz |
1.2 MiB |
1.1.1 |
latest linux/noble R-4.5 | mlpwr_1.1.1.tar.gz |
1.2 MiB |
1.1.1 |
latest source/ R- | mlpwr_1.1.1.tar.gz |
963.0 KiB |
1.1.1 |
2026-04-26 source/ R- | mlpwr_1.1.1.tar.gz |
963.0 KiB |
1.1.1 |
2026-04-23 source/ R- | mlpwr_1.1.1.tar.gz |
963.0 KiB |
1.1.1 |
2026-04-09 windows/windows R-4.5 | mlpwr_1.1.1.zip |
1.2 MiB |
1.1.1 |
2025-04-20 source/ R- | mlpwr_1.1.1.tar.gz |
963.0 KiB |