residual_diagnostics

Performs a battery of statistical tests on model residuals to check
for autocorrelation, heteroscedasticity, and normality.

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

residual_diagnostics function

<dl><dt>residuals</dt>
<dd>Numeric vector of residuals (or model object)</dd>
<dt>data</dt>
<dd>Optional data frame for conditional tests</dd>
<dt>predictors</dt>
<dd>Variable names for heteroscedasticity tests</dd>
<dt>max_lag</dt>
<dd>Maximum lag for autocorrelation tests</dd>
<dt>plot</dt>
<dd>Produce diagnostic plots?</dd></dl>

Arguments

Comprehensive Residual Diagnostics — residual_diagnostics

<dl>

<dt>residuals</dt>
<dd>Numeric vector of residuals (or model object)</dd>


<dt>data</dt>
<dd>Optional data frame for conditional tests</dd>


<dt>predictors</dt>
<dd>Variable names for heteroscedasticity tests</dd>


<dt>max_lag</dt>
<dd>Maximum lag for autocorrelation tests</dd>


<dt>plot</dt>
<dd>Produce diagnostic plots?</dd>

</dl>

residual_diagnostics: Comprehensive Residual Diagnostics

Description

Usage

Value

Arguments