For running psychology and neuroscience experiments
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Updated
Jun 23, 2024 - Python
For running psychology and neuroscience experiments
YACS -- Yet Another Configuration System
A Python-based toolbox of various methods in decision making, uncertainty quantification and statistical emulation: multi-fidelity, experimental design, Bayesian optimisation, Bayesian quadrature, etc.
Bayesian Optimization and Design of Experiments
This Python library implements Trimmed Match for analyzing randomized paired geo experiments and also implements Trimmed Match Design for designing randomized paired geo experiments.
Experimental design and Bayesian optimization library in Python/PyTorch
Active Bayesian Causal Inference (Neurips'22)
Repository collecting resources and best practices to improve experimental rigour in deep learning research.
Lightweight tools for experimental design and multi-objective optimization.
A Python Package for intuitive design of experiments with user-friendly analysis of results. The aim is for this package to rival the DOE capabilities of commercial software such as JMP. Currently designs and analysis will be more geared towards investigations following the Response Surface Methodology.
Repository for the paper "Optimal design of stochastic DNA synthesis protocols based on generative sequence models" (Weinstein et al., AISTATS, 2022).
💫 Computer-aided DNA assembly validation and identification from restriction digests.
Automancer is a software application that enables researchers to design, automate, and manage their experiments.
Python package for flexible generation of D-optimal experimental designs
Code for FLEX, a fast, adaptive and flexible model-based reinforcement learning exploration algorithm.
Bayesian optimization with prescreening of search space via supervised outlier detection
Template structure for behavioural tasks.
The repository for all your experiments
Reproducible configurations for any project.
Stanford Appel Lab - Study Power Project: Data modeling calculator designed with Python and PyQt5 to generate mock data models for power calculations. Designed to aid experimental design. Standalone power and GLM (pairwise t-test and F-test) analysis.
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