coefficient_table

Creates a publication-ready table of estimated coefficients.

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

coefficient_table function

<dl><dt>equation</dt>
<dd>Fitted equation object.</dd>
<dt>bootstrap_results</dt>
<dd>Results from <code>bootstrap_parameters</code> (optional).</dd>
<dt>format</dt>
<dd>Output format: "data.frame", "latex", "markdown", "html".</dd>
<dt>caption</dt>
<dd>Table caption.</dd>
<dt>label</dt>
<dd>LaTeX label for referencing.</dd></dl>

Arguments

Generate Coefficient Table — coefficient_table

<dl>

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


<dt>bootstrap_results</dt>
<dd>Results from <code>bootstrap_parameters</code> (optional).</dd>


<dt>format</dt>
<dd>Output format: "data.frame", "latex", "markdown", "html".</dd>


<dt>caption</dt>
<dd>Table caption.</dd>


<dt>label</dt>
<dd>LaTeX label for referencing.</dd>

</dl>

coefficient_table: Generate Coefficient Table

Description

Usage

Value

Arguments