sensitivity_analysis

Examines how sensitive the model predictions are to parameter perturbations.

A comprehensive toolkit for discovering differential and difference
equations from empirical time series data using symbolic regression. The package
implements a complete workflow from data preprocessing (including Total Variation
Regularized differentiation for noisy economic data), visual exploration of
dynamical structure, and symbolic equation discovery via genetic algorithms.
It leverages a high-performance 'Julia' backend ('SymbolicRegression.jl') to provide
industrial-grade robustness, physics-informed constraints, and rigorous
out-of-sample validation. Designed for economists, physicists, and researchers
studying dynamical systems from observational data.

José Mauricio Gómez Julián

EmpiricalDynamics

Empirical Discovery of Differential Equations from Time Series
Data

sensitivity_analysis function

<dl><dt>equation</dt>
<dd>Fitted equation object.</dd>
<dt>data</dt>
<dd>Data for evaluation.</dd>
<dt>response</dt>
<dd>Name of response column.</dd>
<dt>derivative_col</dt>
<dd>Alias for response.</dd>
<dt>perturbation_pct</dt>
<dd>Percentage perturbation (default 10%).</dd>
<dt>n_bootstrap</dt>
<dd>Number of bootstrap samples for uncertainty.</dd></dl>

Arguments

Parameter Sensitivity Analysis — sensitivity_analysis

<dl>

<dt>equation</dt>
<dd>Fitted equation object.</dd>


<dt>data</dt>
<dd>Data for evaluation.</dd>


<dt>response</dt>
<dd>Name of response column.</dd>


<dt>derivative_col</dt>
<dd>Alias for response.</dd>


<dt>perturbation_pct</dt>
<dd>Percentage perturbation (default 10%).</dd>


<dt>n_bootstrap</dt>
<dd>Number of bootstrap samples for uncertainty.</dd>

</dl>

sensitivity_analysis: Parameter Sensitivity Analysis

Description

Usage

Value

Arguments