EmpiricalDynamics: Empirical Discovery of Differential Equations from Time Series
Data
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.
| Version: |
0.1.2 |
| Depends: |
R (≥ 4.0.0) |
| Imports: |
JuliaCall (≥ 0.17), CVXR (≥ 1.0), minpack.lm (≥ 1.2), signal (≥ 0.7), lmtest (≥ 0.9), tseries (≥ 0.10), ggplot2 (≥ 3.4.0), gridExtra (≥ 2.3), stats, graphics, grDevices, utils, methods |
| Suggests: |
osqp (≥ 0.6), ECOSolveR (≥ 0.5), testthat (≥ 3.0.0), knitr (≥ 1.40), rmarkdown (≥ 2.20), covr, mgcv |
| Published: |
2026-01-16 |
| DOI: |
10.32614/CRAN.package.EmpiricalDynamics (may not be active yet) |
| Author: |
José Mauricio Gómez Julián
 [aut, cre] |
| Maintainer: |
José Mauricio Gómez Julián <isadore.nabi at pm.me> |
| BugReports: |
https://github.com/IsadoreNabi/EmpiricalDynamics/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/IsadoreNabi/EmpiricalDynamics |
| NeedsCompilation: |
no |
| SystemRequirements: |
Julia (>= 1.6) |
| Materials: |
README |
| CRAN checks: |
EmpiricalDynamics results |
Documentation:
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