compute_derivative_spline

Computes derivatives by fitting a smoothing spline and differentiating it.

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

compute_derivative_spline function

<dl><dt>Z</dt>
<dd>Numeric vector of observations.</dd>
<dt>t</dt>
<dd>Numeric vector of time points.</dd>
<dt>spar</dt>
<dd>Smoothing parameter (NULL for automatic selection).</dd>
<dt>df</dt>
<dd>Degrees of freedom (alternative to spar).</dd>
<dt>...</dt>
<dd>Additional arguments (ignored).</dd></dl>

Arguments

Smoothing Spline Derivative — compute_derivative_spline

<dl>

<dt>Z</dt>
<dd>Numeric vector of observations.</dd>


<dt>t</dt>
<dd>Numeric vector of time points.</dd>


<dt>spar</dt>
<dd>Smoothing parameter (NULL for automatic selection).</dd>


<dt>df</dt>
<dd>Degrees of freedom (alternative to spar).</dd>


<dt>...</dt>
<dd>Additional arguments (ignored).</dd>

</dl>

compute_derivative_spline: Smoothing Spline Derivative

Description

Usage

Value

Arguments