Ambrosia is a Python library for A/B tests design, split and result measurement
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Updated
Oct 24, 2023 - Python
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
ABacus: fast hypothesis testing and experiment design solution
Adaptive Design Optimization for Experimental Tasks
Optimal Bayesian Experiment Design
A list of resources for research scientists in psychology who use VR/AR/XR
Raspberry Pi Pico firmware for universal hardware control & measurement, along with a user-friendly Python frontend
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.
A Javascript library to conveniently add distribution builders to your online and offline experiments.
A curated list of awesome resources for science and academia.
Run delayed and risky choice (DARC) experiments using Bayesian Adaptive Design
Python library for adaptive experiment design with state-of-art ML tools
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.
El diseño de experimentos es uno de los pilares de la estadística y la ciencia de datos. En este tutorial te explic, que son, como se hacen y los conceptos clave como intervalos de confianzo, p-values, z-scores y más.
Frame-differencing method: Automatic extraction of movement from video data
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