Empirical Discovery of Differential Equations from Time Series Data R package EmpiricalDynamics version 0.1.3 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. Software Depends: R (>= 4.0.0) Imports: JuliaCall (>= 0.17), CVXR (>= 1.8), 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: clarabel, scs, osqp (>= 1.0), ECOSolveR (>= 0.5), testthat (>= 3.0.0), knitr (>= 1.40), rmarkdown (>= 2.20), covr, mgcv José Mauricio Gómez Julián <isadore.nabi@pm.me> Comprehensive R Archive Network (CRAN) José Mauricio Gómez Julián [aut, cre] (ORCID: <https://orcid.org/0009-0000-2412-3150>) MIT + file LICENSE (https://CRAN.R-project.org/package=EmpiricalDynamics/LICENSE) 2026-03-17 application/tgz https://CRAN.R-project.org/package=EmpiricalDynamics doi:10.32614/CRAN.package.EmpiricalDynamics