mcpower 1.4.1

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Power analysis by simulation — any design from t-test to mixed models, in your browser, on your desktop, or in Python and R.

Why MCPower?

• MCPower covers anything from ANOVA to generalized linear to mixed models. Analytical power formulas exist for a few textbook designs and are correct only when all their assumptions are met (they aren't). Monte Carlo is the ground truth they approximate.
• Fast enough to mean it. A purpose-built engine, hundreds to thousands of times faster per fit than simr, simglm, or Superpower, and 60–1,200× faster than a hand-optimized R/Python loop — a full sample-size search (ten candidate n × 10,000 simulations) runs in seconds, not hours or even days for mixed models. Speed stops being the reason to avoid simulation.
• Robustness built in. Stress-tests your design against the messy, non-ideal data that formulas assume away, so you catch under-powering before you collect.
• Easy, and everywhere. A few-line API across four bindings — Python, R, desktop app, browser. Free and open source.

Install

pip install mcpower

Plotting is an optional extra: pip install mcpower[plot] (needed for save_plot() and inline Jupyter plots).

numpy and pandas are not required — they're accepted only as optional input formats for upload_data() and set_correlations() (plain Python lists and dicts work everywhere). Install them with pip install mcpower[optional] if you want to pass arrays or DataFrames.

Quickstart

from mcpower import MCPower

# Clinical trial testing a new therapy vs control.
# Research question: Does the new therapy improve patient outcomes?

# Define the model with an R-style formula.
model = MCPower("patient_outcome = treatment + baseline_score")

# Expected effect sizes (standardised).
#   treatment=0.5      → therapy shifts outcomes by 0.5 SD (a medium effect).
#   baseline_score=0.3 → baseline moderately predicts the outcome.
model.set_effects("treatment=0.5, baseline_score=0.3")

# Variable types — treatment is binary (0=control, 1=therapy).
model.set_variable_type("treatment=binary")

# Power at N=120, targeting the treatment test.
# One row per effect with Power, 95% CI, and a ✓/✗ marker against target power.
model.find_power(sample_size=120, target_test="treatment")

More examples

Sample size search:

from mcpower import MCPower

# Educational intervention study.
# Research question: What N do we need to detect the intervention effect?

model = MCPower("test_score = intervention + prior_knowledge + motivation")

model.set_effects("intervention=0.4, prior_knowledge=0.35, motivation=0.3")

# Variable types — intervention is binary (0=control, 1=intervention).
model.set_variable_type("intervention=binary")

# Sweep a grid and report the smallest N that reaches target power.
model.find_sample_size(target_test="intervention", from_size=30, to_size=300, by=10)

Mixed-effects / LME (clustered data):

from mcpower import MCPower

# Education study where students are nested in classrooms.
# Research question: Does a teaching method raise test scores, accounting for
# the fact that students in the same classroom are correlated?

# Declare a mixed model. The (1|classroom) term adds a random intercept per
# classroom; family="lme" fits it by maximum likelihood (MLE estimator).
model = MCPower("score = teaching_method + prior_gpa + (1|classroom)", family="lme")
model.set_variable_type("teaching_method=binary")
model.set_effects("teaching_method=0.4, prior_gpa=0.18")

# Describe the clustering: ICC=0.15 (15% of variance is between-classroom)
# across 30 classrooms. At N=300 that is 10 students per classroom.
model.set_cluster("classroom", ICC=0.15, n_clusters=30)

# Power at N=300 for the fixed effects.
model.find_power(sample_size=300, target_test="teaching_method, prior_gpa")

See examples/ (01–11) and the Python tutorial for interactions, correlations, factors/ANOVA, logistic regression, your-own-data upload, custom scenarios, and plotting.

API at a glance

Two entry points, a fluent set_* chain:

All set_* methods chain and return self; add scenarios=True to either find_* for optimistic/realistic/doomer robustness.

Docs

Full documentation: https://docs.mcpower.app.

Citation & License

GPL v3. If you use MCPower in research, please cite:

Lenartowicz, P. (2025). MCPower: Monte Carlo Power Analysis for Complex Statistical Models [Computer software]. Zenodo. https://doi.org/10.5281/zenodo.16502734

@software{mcpower2025,
  author    = {Lenartowicz, Pawe{\l}},
  title     = {{MCPower}: Monte Carlo Power Analysis for Complex Statistical Models},
  year      = {2025},
  publisher = {Zenodo},
  doi       = {10.5281/zenodo.16502734},
  url       = {https://doi.org/10.5281/zenodo.16502734}
}

Paweł Lenartowicz — Freestyler Scientist · GitHub · ORCID