logistic-growth

What problem does it solve?

This Skill provides a structured approach to fitting a logistic growth model to time-series data, enabling estimation of the intrinsic growth rate r and carrying capacity K, which are essential for understanding bounded growth in populations, epidemiology, and market dynamics.

Core Features & Use Cases

• Analytical & numerical fitting: supports nonlinear least squares and linearized methods to estimate N0, r, and K from observed data.
• Interpretability: exposes analytical solutions, the inflection point, and stability insights (N=0 unstable, N=K stable) for clear interpretation.
• Visualization & forecasting: offers plotting utilities to compare observed data with model predictions and to visualize growth dynamics over time.

Quick Start

• Prepare your time points t (e.g., days) and observed population N(t).
• Run nonlinear fitting to estimate N0, r, and K with fit_nonlinear(t, N) or use fit_linearized(t, N) for a quick check.
• Inspect the fitted parameters (N0, r, K) and goodness-of-fit, then visualize the results with plot_logistic_fit.