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

Workshop on basic machine learning, computational modeling, psychophysics, basic data analysis and experiment design

Adaptive Design Optimization for Experimental Tasks

ABacus: fast hypothesis testing and experiment design solution

Optimal Bayesian Experiment Design

User-friendly firmware+module to control Rasbperry Pi Pico directly from Python scripts. Set up digital or analog input/output, steppers and servos positioning etc. in a minute.

A list of resources for research scientists in psychology who use VR/AR/XR

A Python Library for Implementing Human-Computer Interface Experiments

A curated list of awesome resources for science and academia.

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

A Javascript library to conveniently add distribution builders to your online and offline experiments.

A long-form analytics article on why KPIs fail when the denominator silently changes. Covers denominator contracts, eligibility vs observed vs attempted populations, drift/coverage failure modes, mix shifts, time alignment, and a practical audit checklist to design decision-safe ratio metrics that don’t lie.

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

A practical framework for turning data analysis into decision policies you can defend. Covers risk modeling, thresholding, exception handling, policy cards, monitoring, and update triggers, using real patterns like abstention rules, reorder points, and fairness-aware benchmarking. Built for “ship it” data science.

Run delayed and risky choice (DARC) experiments using Bayesian Adaptive Design

Design experiments distributed in several batches

A walk through A/B Tests for new feature

PsyGlass: An open-source framework for implementing Google Glass in research settings

Simulation tool for optimal design of high-dimensional MS-based proteomics experiment

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