print.residual_diagnostics

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

print.residual_diagnostics function

<dl><dt>x</dt>
<dd>Object of class residual_diagnostics</dd>
<dt>...</dt>
<dd>Additional arguments (ignored)</dd></dl>

Arguments

Print Residual Diagnostics — print.residual_diagnostics

<dl>

<dt>x</dt>
<dd>Object of class residual_diagnostics</dd>


<dt>...</dt>
<dd>Additional arguments (ignored)</dd>

</dl>

print.residual_diagnostics: Print Residual Diagnostics

Description

Usage

Value

Arguments