pydrc is a powerful Python module specially designed for the analysis and visualization of dose-response data in fields like toxicology, pharmacology, and environmental sciences.
The package simplifies the process of implementing various dose-response models, offering a uniform interface for a wide range of common models, including but not limited to Hill, Logistic, Gompertz models, and more.
- Wide Range of Models: Implementation of a broad selection of dose-response models.
- Robust Estimation: Parameter estimation using state-of-the-art optimization algorithms.
- Model Evaluation: Tools for the evaluation of model performance and selection.
- Data Visualization: Aesthetic and intuitive visualization of dose-response curves using Matplotlib and Seaborn.
- Flexibility: Capability to handle user-defined models.
Built for the scientific community, pydrc bridges the gap between intricate dose-response analyses and Python's ease of use, empowering researchers to concentrate on interpreting results instead of wrestling with the coding of analyses.
Contributions are welcome.
- Implementation of multiple optimization algorithms for existing functions (Current: Levenberg–Marquardt algorithm for unconstrained optimization; Trust Region Reflective for constrained optimization)
- Implement superfunction for data input, variable arguments and specified function to be optimized (built-in functions for now)
- Introduce functions for effective dose estimation and benchmark dosing
- Curve ID argument for summary table and visualization of multiple treatment groups
- Automatic and customizable dose-response curve visualization in Matplotlib and Seaborn with **kwargs
- Integrating and testing each function