Ambrosia is a Python library for A/B tests design, split and result measurement

ABacus: fast hypothesis testing and experiment design solution

Adaptive Design Optimization for Experimental Tasks

Optimal Bayesian Experiment Design

A Python Library for Implementing Human-Computer Interface Experiments

🔨 Malet (Machine Learning Experiment Tool) is a tool for efficient machine learning experiment execution, logging, analysis, and plot making.

Python library for adaptive experiment design with state-of-art ML tools

An application to easily set up and run online listening experiments for music research.

a minimalist functioning code module and folder structure, built to teach and exercise with computer-controlled measurements using Python.

exp-runner: Simple and extensible Python framework for data analysis and machine learning experiments

This Streamlit application simulates A/B tests, providing a platform to evaluate the performance of different statistical tests based on data distribution.

synapz is a research prototype exploring how large language models can adapt teaching content to different cognitive styles. built over a 48-hour sprint with a strict $50 api budget, this project implements a scientific framework to test whether adaptive teaching produces measurably better results than static approaches.

Uses the iterative and recursive approaches to generate strobogrammatic numbers and binary search and runs experiments. Also creates an algorithm that combines merge with insertion sort.

Reproducible, efficient, flexible batch processing using pipelines for sustainable software experiments

Experiment-as-Market (EXAM)

A/B test user splitter PoC, mainly for research and toy projects

Lightweight python experiment tracker.

Flexible ball-drag-and-drop apps for oTree for dictator games or rule-following tasks.

Uplift modeling and estimation via the "Uplift Support Vector Machine".