Many scientific workflows rely on derivatives of complex models: Fisher forecasts, sensitivity analysis, gradient-based inference, and emulator construction. In practice, these derivatives are often difficult to compute reliably and integrate into end-to-end inference pipelines.
DerivKit is an open-source Python toolkit that provides a unified framework for derivative-based scientific inference. It supports multiple derivative backends and connects model evaluation directly to downstream inference tools, including Fisher analyses and higher-order likelihood approximations. The framework also provides diagnostics and visualization tools for exploring parameter sensitivities and degeneracies.
Originally developed for cosmological forecasting pipelines, DerivKit is designed to be domain-agnostic and easily integrated into scientific Python workflows.