Cascade-HMM is a Python toolkit for modelling and analysing time-resolved fluorescence and single-molecule data using Hidden Markov Models (HMMs) and cascade-based state inference approaches.
The package is designed for quantitative biophysics and fluorescence imaging applications, where stochastic state transitions, noisy observations, and temporal structure must be inferred robustly from data.
- Hidden Markov Model–based state inference
- Support for cascade or multi-stage transition models
- Tools for fitting, simulation, and validation
- Designed for time-resolved fluorescence and single-molecule data
- Modular architecture for integration into custom analysis pipelines
Clone the repository and install dependencies:
git clone https://github.com/C-Trass/Cascade-HMM.git
cd Cascade-HMM
pip install -r requirements.txtCascade-HMM is intended for:
- Researchers analysing time-resolved or stochastic biophysical data
- FLIM, FRET, and single-molecule users requiring state inference
- Users moving beyond threshold-based or heuristic analyses
- Scientists who value transparent, reproducible statistical models
If you use Cascade-HMM in academic work, please cite:
Dr Conor A. Treacy, Cascade-HMM: Hidden Markov modelling for time-resolved fluorescence data analysis, Biomedical Optics Express, forthcoming.
A DOI will be added here once the associated methods paper is published.
Until then, you may cite this repository directly using the GitHub URL and the software version.
This project is released under the MIT License. You are free to use, modify, and distribute the code with attribution.