mlpwr
1.1.1A Power Analysis Toolbox to Find Cost-Efficient Study Designs
Overview
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).
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Health
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- NOTE2026-05-020 OK · 13 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-04-250 OK · 11 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- NOTE2026-03-103 OK · 11 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 83%
- Documented parameters
- 100%
- Return-value docs
- 100%
- References docs
- 0%
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Code & Tests
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Package metadata
- First published
- 2022-10-14
- Total releases
- 4 / 4 yrs
- License
- GPL (>= 3) OSI
- Minimum R
- ≥ 3.5.0
- Bundled data
- 162 KB / 1 file
- Download size
- 964 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet
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