plot_bivariate

Creates a scatter plot with optional nonparametric fit and marginal
distributions.

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

plot_bivariate function

<dl><dt>data</dt>
<dd>Data frame.</dd>
<dt>x_var</dt>
<dd>X variable name.</dd>
<dt>y_var</dt>
<dd>Y variable name.</dd>
<dt>color_var</dt>
<dd>Optional variable for color mapping.</dd>
<dt>show_fit</dt>
<dd>Add smooth fit?</dd>
<dt>show_marginals</dt>
<dd>Add marginal histograms?</dd></dl>

Arguments

Bivariate Scatter Plot — plot_bivariate

<dl>

<dt>data</dt>
<dd>Data frame.</dd>


<dt>x_var</dt>
<dd>X variable name.</dd>


<dt>y_var</dt>
<dd>Y variable name.</dd>


<dt>color_var</dt>
<dd>Optional variable for color mapping.</dd>


<dt>show_fit</dt>
<dd>Add smooth fit?</dd>


<dt>show_marginals</dt>
<dd>Add marginal histograms?</dd>

</dl>

plot_bivariate: Bivariate Scatter Plot

Description

Usage

Value

Arguments