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>).

Package details

AuthorFelix Zimmer [aut, cre] (<https://orcid.org/0000-0002-8127-0007>), Rudolf Debelak [aut] (<https://orcid.org/0000-0001-8900-2106>), Marc Egli [ctb]
MaintainerFelix Zimmer <felix.zimmer@mail.de>
LicenseGPL (>= 3)
Version1.1.1
URL https://github.com/flxzimmer/mlpwr
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("mlpwr")

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mlpwr documentation built on Oct. 4, 2024, 1:07 a.m.