fit_residual_distribution

Fits candidate probability distributions to residuals, optionally with
parameters that depend on state variables.

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

fit_residual_distribution function

<dl><dt>residuals</dt>
<dd>Numeric vector of residuals</dd>
<dt>candidates</dt>
<dd>Character vector of distribution families to try</dd>
<dt>conditional_on</dt>
<dd>Formula for conditional parameters (optional)</dd>
<dt>data</dt>
<dd>Data frame (required if conditional_on specified)</dd></dl>

Arguments

Fit Residual Distribution — fit_residual_distribution

<dl>

<dt>residuals</dt>
<dd>Numeric vector of residuals</dd>


<dt>candidates</dt>
<dd>Character vector of distribution families to try</dd>


<dt>conditional_on</dt>
<dd>Formula for conditional parameters (optional)</dd>


<dt>data</dt>
<dd>Data frame (required if conditional_on specified)</dd>

</dl>

fit_residual_distribution: Fit Residual Distribution

Description

Usage

Value

Arguments