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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>).
Package details |
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Author | Felix Zimmer [aut, cre] (<https://orcid.org/0000-0002-8127-0007>), Rudolf Debelak [aut] (<https://orcid.org/0000-0001-8900-2106>), Marc Egli [ctb] |
Maintainer | Felix Zimmer <felix.zimmer@mail.de> |
License | GPL (>= 3) |
Version | 1.1.1 |
URL | https://github.com/flxzimmer/mlpwr |
Package repository | View on CRAN |
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