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extraSuperpower

R package for two-way factorial design sample size calculation. This is performed in three steps. The package:

  1. Calculates expected outcomes into a cell mean model.
  2. Simulates the data
  3. Estimates the power for a given sample size

These steps allow for independent and repeated measures experiments with balanced or unbalanced design.

For the first step we provide a function to create mean values and standard deviation matrices. For repeated measures designs correlation and covariance matrices are also generated. For the second step separate functions are used to simulate independent and repeated measures experiments. Once the two-way factorial study is simulated under different sample sizes, the power under different statistical tests for these sample sizes can be estimated.

extraSuperpower depends on packages stringr, ggplot2, reshape2, scales, Matrix, ggpubr, ggthemes, rlist, fGarch, truncnorm, MASS, sn, tmvtnorm, afex, Rfit, permuco and nparLD.

The package is available in CRAN. To install in R.

install.packages("extraSuperpower")

First steps:

library(extraSuperpower)

?calculate_mean_matrix ## example of one between, one within (repeated measures) factorial design simulation

?test_power_overkn ## example of independent measures sample size calculation with plot

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Install

install.packages('extraSuperpower')

Version

1.5.2

License

MIT + file LICENSE

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Maintainer

Louis Macias

Last Published

July 24th, 2025

Functions in extraSuperpower (1.5.2)

gencovariancemat

Function that generates a covariance matrix taking as input a correlation matrix and a standard deviation matrix or value.
graph_twoway_assumptions

Graph modeled means and standard deviations of groups in two-way factorial design
twoway_simulation_correlated

Simulate measurements repeated over either or both factors of a two-way design
simulate_twoway_nrange

Simulated independent and repeated measures two-way experiments over a set of sample sizes
calculate_mean_matrix

Create input for simulation based two-way factorial experiments
exact_twoway_anova_power

Two-way factorial ANOVA exact sample size calculation for independent samples
effsize

Effect size calculation
plot_powercurves

Plots the output of test_twoway_nrange
gencorrelationmat

Function that generates a correlation matrix taking as input number of factors for each level, factor or factors that present correlation and rho value or values. Additionally, a mean matrix is required to check consistency.
test_power_overkn

Test simulated two-way factorial design experiments over different sample sizes.
twoway_simulation_independent

Simulate independent measurements in a two-way factorial design
twoway_simulation_testing

Calculate power for global main effects and interaction from two-way factorial simulated data