Package: mlpwr 1.1.1.9000

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 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>).

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

mlpwr_1.1.1.9000.tar.gz
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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
mlpwr/json (API)

# Install 'mlpwr' in R:
install.packages('mlpwr', repos = c('https://flxzimmer.r-universe.dev', 'https://cloud.r-project.org'))

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

Datasets:

On CRAN:

Conda:

5.66 score 5 stars 26 scripts 337 downloads 3 exports 27 dependencies

Last updated from:133bfe8a91. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK177
source / vignettesOK1931
linux-release-x86_64OK220
macos-release-arm64OK158
macos-oldrel-arm64OK194
windows-develOK103
windows-releaseOK102
windows-oldrelOK105
wasm-releaseOK177

Exports:example.simfunfind.designsimulations_data

Dependencies:clicpp11data.tableDiceKrigingdigestfarverggplot2gluegtableisobandjsonlitelabelinglifecycleR6randtoolboxRColorBrewerrgenoudrlangrlistrngWELLS7scalesvctrsviridisLitewithrXMLyaml

ANOVA Application
ANOVA Vignette | Introduction to mlpwr | Introduction to ANOVA | Assumptions | Formula | Scenario | Power Analysis | Data Preparation | Cost function
Rendered fromANOVA_Vignette.Rmdusingknitr::rmarkdown

Last update: 2024-10-04
Started: 2023-09-08

GLM Application
Generalized Linear Models (GLM) Vignette | Introduction to mlpwr | Introduction to Generalized Linear Models (GLMs) - The Poisson Model | Assumptions of the Poisson Model | Formula for the Poisson Model | Scenario | Power Analysis | Data Preparation
Rendered fromGLM_Vignette.Rmdusingknitr::rmarkdown

Last update: 2024-10-04
Started: 2023-09-08

IRT Application
Item Response Theory (IRT) Vignette | Introduction to mlpwr | Rasch vs. 2PL | Introduction to the Rasch and 2PL Model | Scenario | Set Up | Data Preparation | Model fit | Power Analysis | DIF Analysis | Cost function
Rendered fromIRT_Vignette.Rmdusingknitr::rmarkdown

Last update: 2024-10-04
Started: 2023-09-08

Multilevel Application
Mixed Models Vignette | Introduction to mlpwr | Generalized Linear Mixed Models: Poisson | Introduction to Generalized Linear Mixed Models (GLMMs) | The Poisson Model | Assumptions of the Poisson Model | Formula of the Poisson Model | Scenario | Set Up | Data preparation | Power Analysis | Simulating from an existing model | Data Preparation | Cost function
Rendered fromMLM_Vignette.Rmdusingknitr::rmarkdown

Last update: 2024-10-04
Started: 2023-09-08

t-test Application
T-Test Vignette | Introduction to mlpwr | Introduction to Two-Sample t-test | Assumptions | Welch's t-test | Formula | Performing Welch's T-Test in R | Scenario | Power Analysis | Data Preparation | Cost function
Rendered fromttest_Vignette.Rmdusingknitr::rmarkdown

Last update: 2024-10-04
Started: 2023-09-08

Extensions
Beyond Power Analysis | Sensitivity Analysis | Compromise Analysis | Inhomogeneous Cost Functions | 3-Dimensional Designs
Rendered fromextensions.Rmdusingknitr::rmarkdown

Last update: 2023-08-07
Started: 2023-01-07

simulation_functions
T-Test | ANOVA | Generalized Linear Models | Using a fitted model | Specifying parameters manually | Item Response Theory Models | Testing a Rasch against a 2PL model | Testing for DIF | Multilevel Models
Rendered fromsimulation_functions.Rmdusingknitr::rmarkdown

Last update: 2023-07-26
Started: 2022-09-23