Package: anovapowersim 1.1.0.9000

anovapowersim: Simple Power Simulations for ANOVAs

A-priori power simulations and power-calculations for within, between and mixed ANOVAs based on target (partial) eta-squared values. Supports complex designs with more than two factors and their interactions with a single function call.

Authors:Shaheed Azaad [aut, cre]

anovapowersim_1.1.0.9000.tar.gz
anovapowersim_1.1.0.9000.zip(r-4.7-any)anovapowersim_1.1.0.9000.zip(r-4.6-any)anovapowersim_1.1.0.9000.zip(r-4.5-any)
anovapowersim_1.1.0.9000.tgz(r-4.6-any)anovapowersim_1.1.0.9000.tgz(r-4.5-any)
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anovapowersim_1.1.0.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
anovapowersim/json (API)

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

Bug tracker:https://github.com/shaheedazaad/anovapowersim/issues

Pkgdown/docs site:https://shaheedazaad.github.io

On CRAN:

Conda:

5.32 score 7 scripts 350 downloads 17 exports 75 dependencies

Last updated from:0293042b07. Checks:6 OK, 3 ERROR. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK360
source / vignettesOK231
linux-release-x86_64OK370
macos-release-arm64OK219
macos-oldrel-arm64OK192
windows-devel-x86_64ERROR303
windows-release-x86_64ERROR319
windows-oldrel-x86_64ERROR279
wasm-releaseOK139

Exports:balanced_anova_designcell_designcompute_scale_factordesign_term_meansmeans_patternplot_power_curvepower_achievedpower_achieved_calcpower_curvepower_npower_n_calcpower_sensitivitypower_sensitivity_calcpower_unbalancedsimulate_design_datasetunbalanced_covariancewithin_covariance

Dependencies:abindbackportsbootbroomcarcarDataclicodetoolscolorspacecowplotcpp11DerivdigestdoBydplyrfarverforecastFormulafracdifffuturefuture.applygenericsggplot2globalsgluegtableisobandlabelinglatticelifecyclelistenvlme4lmtestmagrittrMASSMatrixMatrixModelsmgcvminqamodelrnlmenloptrnnetnumDerivparallellypbkrtestpillarpkgconfigpurrrquantregR6rbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreformulasrlangS7scalesSparseMstringistringrsurvivaltibbletidyrtidyselecttimeDateurcautf8vctrsviridisLitewithrzoo

Covariance and nonsphericity
Name the within-subject cells | Use the covariance specification | Why the relative means pattern matters | The default linear/Kronecker direction | The two sparse-cell interfaces | Calculation-only and unbalanced designs
Rendered fromcovariance.Rmdusingknitr::rmarkdown

Last update: 2026-07-22
Started: 2026-07-22

Comparison with G*Power
A typical 2 x 2 design | More than three within-subject levels
Rendered fromcomparison-with-gpower.Rmdusingknitr::rmarkdown

Last update: 2026-07-22
Started: 2026-07-22

Power for unbalanced designs
Define the cells | Define within-subject correlations | Interpret the result | Multiple within-subject factors
Rendered fromunbalanced-designs.Rmdusingknitr::rmarkdown

Last update: 2026-07-22
Started: 2026-07-22

Calculated power without simulations
Required sample size | Achieved power | Sensitivity | Planned nonsphericity | Comparison with G*Power | When to use simulations instead
Rendered fromcalculated-power.Rmdusingknitr::rmarkdown

Last update: 2026-07-22
Started: 2026-07-22

Fixed-sample power and sensitivity
Achieved power | Sensitivity | Covariance and calculated-power alternatives
Rendered fromfixed-sample-power.Rmdusingknitr::rmarkdown

Last update: 2026-07-22
Started: 2026-07-22

Getting started with anovapowersim
Search for the required sample size | Adding factors and levels | Simulate a power curve | Run simulations in parallel | Next steps
Rendered fromanovapowersim.Rmdusingknitr::rmarkdown

Last update: 2026-07-22
Started: 2026-05-05

Readme and manuals

Help Manual

Help pageTopics
Create a balanced factorial ANOVA design specificationbalanced_anova_design
Define cells for a means-based unbalanced ANOVA designcell_design
Compute the mean-deviation scaling factor from a change in partial eta squaredcompute_scale_factor
Build calibrated means for a design termdesign_term_means
Define a sparse relative cell-mean patternmeans_pattern
Plot a simulation-based power curveplot_power_curve
Estimate achieved ANOVA power at a fixed sample sizepower_achieved
Calculate achieved ANOVA power at a fixed sample sizepower_achieved_calc
Simulate ANOVA power from a balanced factorial designpower_curve
Search for the sample size needed for target ANOVA powerpower_n
Calculate the sample size needed for target ANOVA powerpower_n_calc
Estimate ANOVA effect-size sensitivity at a fixed sample sizepower_sensitivity
Calculate ANOVA effect-size sensitivity at a fixed sample sizepower_sensitivity_calc
Simulate power for a fixed unbalanced ANOVA designpower_unbalanced
Print a fixed-sample achieved-power resultprint.anovapowersim_achieved_power
Print an anovapowersim power curveprint.anovapowersim_curve
Print a fixed-sample sensitivity resultprint.anovapowersim_sensitivity
Print simulated power for an unbalanced designprint.anovapowersim_unbalanced_power
Simulate data from a balanced ANOVA designsimulate_design_dataset
Summarise a fixed-sample achieved-power resultsummary.anovapowersim_achieved_power
Summarise an anovapowersim power curvesummary.anovapowersim_curve
Summarise a fixed-sample sensitivity resultsummary.anovapowersim_sensitivity
Summarise simulated power for an unbalanced designsummary.anovapowersim_unbalanced_power
Specify covariance for a means-based unbalanced designunbalanced_covariance
Specify a within-subject covariance structurewithin_covariance