Analysis Toolbox
A versatile Python toolkit for data analysis, statistical modeling, and scientific computing across research domains.
What is the Analysis Toolbox?
The Analysis Toolbox is a continuously evolving collection of analysis methods and computational tools built for scientific research. While it originated from work with physiological signals (EEG, fNIRS, ECG), it has grown into a general-purpose analysis framework designed to handle diverse data analysis challenges across different research domains.
Rather than being a fixed toolkit, it's an active development project that expands based on real research needs. Missing modules are added as new analytical challenges arise, and the architecture is designed to accommodate everything from time-series analysis and statistical modeling to machine learning pipelines and custom computational methods.
The toolbox provides validated, reusable components built on scientific Python libraries (NumPy, SciPy, Pandas, Statsmodels). Whether you're processing sensor data, running statistical analyses, building data pipelines, or developing custom algorithms, the toolbox offers tested building blocks that integrate into your workflow. All code follows open science principles and welcomes contributions—if you're missing a module or have suggestions, they're actively considered for implementation.
Core Capabilities
- Signal Processing — Time-series analysis, filtering, spectral methods, and feature extraction
- Statistical Analysis — Univariate and multivariate testing, regression, and statistical modeling
- Data Pipeline Tools — Preprocessing, quality assurance, and automated workflows
- Machine Learning Integration — Scikit-learn compatible transformers and utilities
- Physiological Computing — Specialized methods for EEG, fNIRS, ECG, and other biosignals
- Extensible Architecture — Modular design for adding custom analysis methods
- Open Development — MIT licensed, documented, and accepting contributions