ed_theme

A clean, publication-ready theme for all diagnostic plots.

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

ed_theme function

<dl><dt>base_size</dt>
<dd>Base font size.</dd>
<dt>base_family</dt>
<dd>Base font family.</dd></dl>

Arguments

Default ggplot2 Theme for EmpiricalDynamics — ed_theme

<dl>

<dt>base_size</dt>
<dd>Base font size.</dd>


<dt>base_family</dt>
<dd>Base font family.</dd>

</dl>

ed_theme: Default ggplot2 Theme for EmpiricalDynamics

Description

Usage

Value

Arguments