Research Expert

This is a solid implementation for anyone doing quantitative research or academic work. It handles the statistical fundamentals you actually need: sample size calculations for power analysis, multiple randomization schemes for experimental design, and the standard hypothesis tests with effect sizes built in. The code includes things like block and stratified randomization, multiple testing corrections, and proper effect size calculations like Cohen's d and Hedges' g. It's Python heavy with scipy and statsmodels, so you'll want those dependencies ready. Best for researchers who need to design experiments, analyze results, or understand statistical approaches rather than just run black box tests. The structure is clear enough that you can adapt the methods to your specific research context.