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anovapowersim

anovapowersim is designed to make determining a priori power for ANOVAs as easy as possible. You can add as many within/between factors with as many levels as you would like. There’s no need to estimate condition means, SDs, or repeated-measures correlations; just enter the target partial eta squared.

The package simulates data and estimates power based on the specified design. It also provides direct power calculations for comparison.

Getting a priori power for a 2 × 2 × 3 mixed interaction effect is as simple as running the following:

install.packages("anovapowersim") # if not already installed

library(anovapowersim)

power_n(
  between = c(group = 2), # group has 2 levels
  within = c(stim = 2, cond = 3), # stim has 2 levels, cond has 3
  term = "group:stim:cond", # three-way interaction term
  target_pes = 0.08, # target effect size
  n_sims = 5000, # increase to 10000+ for more precise estimates
  power = .90,
  alpha = .05,
  parallel = TRUE, # simulations will be run in parallel for speed
  seed = 123 # for reproducibility
)

#><anovapowersim_curve>
#>  term:          'group:stim:cond'
#>  target power:  0.900
#>  alpha:         0.05
#>  effect size:   pes = 0.08
#>  n values:      8 per-cell sample sizes visited
#>  sims per cell size: 5000
#>  SS type:       III
#>  n needed for between-subjects cell: 38
#>  total N needed: 76
#>
#> n_per_cell total_n n_sims num_df den_df    ncp power_calc power_sim
#>         31      62   5000      2    120 10.435      0.823     0.825
#>         37      74   5000      2    144 12.522      0.890     0.885
#>         38      76   5000      2    148 12.870      0.899     0.903
#>         39      78   5000      2    152 13.217      0.907     0.901
#>         40      80   5000      2    156 13.565      0.915     0.918
#>         41      82   5000      2    160 13.913      0.922     0.916
#>         46      92   5000      2    180 15.652      0.949     0.947
#>         62     124   5000      2    244 21.217      0.989     0.988

Additional power analyses

anovapowersim includes several experimental power-analysis options. Their full examples and guidance are kept in the dedicated guides linked below.

Achieved power and sensitivity

At a fixed sample size, power_achieved() estimates power for a chosen partial eta squared, while power_sensitivity() estimates the minimum detectable partial eta squared. See the fixed-sample tutorial.

Calculation-only functions

The _calc() functions skip simulations and use calculated noncentral-F power. They also support planned nonsphericity through epsilon. See the calculated-power tutorial.

Power for unbalanced designs

power_unbalanced() simulates one exact allocation from user-defined cell means and sample sizes under a common standard deviation and optional within-subject correlations. It is simulation-only and does not extrapolate how unequal cell sizes should scale. See the unbalanced-design tutorial.

Installation

Install anovapowersim from CRAN:

install.packages("anovapowersim")

CRAN remains the primary installation source. R-universe also provides published GitHub Releases rather than development snapshots.

You can install the development version from GitHub:

install.packages("pak")
pak::pak("shaheedazaad/anovapowersim")

Or, with remotes:

install.packages("remotes")
remotes::install_github("shaheedazaad/anovapowersim")

Citation

Azaad, S. (2026). A priori power analysis for ANOVA interaction effects with the anovapowersim R package: a short introduction. https://doi.org/10.31234/osf.io/86rsy_v1.

Limitations

anovapowersim is designed to be simple and easy to use first, which means it has some limitations for now. It does not support:

• Covariates (ANCOVAs)
• Sample-size searches or power curves for unbalanced designs
• Huynh-Feldt corrections in power simulations. Greenhouse-Geisser-corrected simulated tests are supported for sums-of-squares type II or III when a custom covariance implies epsilon < 1; type I tests remain uncorrected.
• Heteroskedastic ANOVA. Simulation functions require one common marginal variance; unequal correlations and Greenhouse–Geisser corrections remain supported for repeated-measures designs.
• Simple main effects/pairwise comparisons

Other packages

I recommend checking out Superpower, which handles some of the limitations above.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.