compute_derivatives

Convenience function to compute derivatives for all endogenous variables
in a specified dataset.

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

compute_derivatives function

<dl><dt>data</dt>
<dd>Data frame with variable specifications (from <code>specify_variables</code>).</dd>
<dt>method</dt>
<dd>Differentiation method.</dd>
<dt>prefix</dt>
<dd>Prefix for derivative column names (default "d_").</dd>
<dt>...</dt>
<dd>Additional arguments passed to <code>compute_derivative</code>.</dd></dl>

Arguments

Compute Derivatives for Specified Variables — compute_derivatives

<dl>

<dt>data</dt>
<dd>Data frame with variable specifications (from <code>specify_variables</code>).</dd>


<dt>method</dt>
<dd>Differentiation method.</dd>


<dt>prefix</dt>
<dd>Prefix for derivative column names (default "d_").</dd>


<dt>...</dt>
<dd>Additional arguments passed to <code>compute_derivative</code>.</dd>

</dl>

compute_derivatives: Compute Derivatives for Specified Variables

Description

Usage

Value

Arguments