Machine Learning vs Statistical Methods for Time Series Forecasting: Size Matters

Minimal A/B Testing Library in PHP

Design of single- and multi-stage multi-arm clinical trials

This python project is a helper package that uses power analysis to calculate required sample size for any experiment

A web application to design and evaluate the results of A/B tests.

Free WordPress Plugin: Calculate the exact sample size and margin of error for your next survey or study. Use our free Sample Size Calculator for statistically accurate results. www.calculator.io/sample-size-calculator/

Survey Planning Tools

Project repository of the Matters Arising commentary "Multivariate BWAS can be replicable with moderate sample sizes in some cases""

Tools for power analysis, sample size requirements, minimum detectable effects (MDE) calculation

MinSizeML (Minimum Size for Machine Learning) R package that contains functions to estimate the minimum sample size for machine learning

Collection of scripts and modules for biostatistics and data science simple automation for research process.

Calculate statistical power for longitudinal linear mixed-effects models with dropout. Supports null hypothesis, non-inferiority, and equivalence tests.

Code for the paper "Comparison of discrimination and calibration performance of ECG-based machine learning models for prediction of new-onset atrial fibrillation"

Simulation-based sample size tools for prediction models

Power and Sample Size Calculation for the Cochran-Mantel-Haenszel Chi-Squared Test

A Power Analysis and Sample Size Calculation Toolkit for Python

jamovi wrapper for pwrss package

Sample size and power for association studies involving mitochondrial DNA haplogroups -

A/B tests calc

Claude Code plugin & MCP server for Phase 2/3 clinical trial design. 9 tools: single-primary (binary/continuous/PH+NPH survival), multi-hypothesis (co-primary, multi-population, Maurer-Bretz). Backed by gsDesign, gsDesign2, graphicalMCP. Word/PDF reporting, Monte-Carlo verification, 176-case benchmark corpus.