Package: mlpwr 1.1.0.9000

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 a recent preprint (Zimmer & Debelak (2022) <doi:10.31234/osf.io/tnhb2>). 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.

Authors:Felix Zimmer [aut, cre], Rudolf Debelak [aut], Marc Egli [ctb]

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Bug tracker:https://github.com/flxzimmer/mlpwr/issues

On CRAN:

3 exports 4 stars 1.26 score 39 dependencies 12 scripts 287 downloads

Last updated 5 months agofrom:e8725662bc. Checks:OK: 7. Indexed: yes.

Exports:example.simfunfind.designsimulations_data

Dependencies:clicolorspacedata.tableDiceKrigingdigestfansifarverggplot2gluegtableisobandjsonlitelabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6randtoolboxRColorBrewerrgenoudrlangrlistrngWELLscalestibbleutf8vctrsviridisLiteWeightSVMwithrXMLyaml