Nothing
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>).
Getting started
Browse package contents
Package details |
|
|---|---|
| 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 |
| Installation |
Install the latest version of this package by entering the following in R:
|
Try the mlpwr package in your browser
Any scripts or data that you put into this service are public.
