Biomedical experiments usually measure the same biological units more than once: the same cells, animals, or subjects across conditions, time points, or nested designs. Standard power calculations do not account for the correlation that creates. RMeDPower2 does.

What it does

• Simulation-based power and sample size calculation using mixed-effects models
• Two-level outlier detection, at both the observation and the group level
• Testing of modeling assumptions with diagnostic visualizations
• Normal, binomial, Poisson, and negative binomial distributions
• Estimation and visualization of effect sizes for the predictors you care about

Typical use: cell-based assays, single-cell RNA-seq, animal behavioral studies, and electrophysiology with nested or crossed designs.

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