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.

Empirical likelihood ratio tests and confidence intervals for means/quantiles/hazards
 	from possibly censored and/or truncated data. In particular, the empirical likelihood
        for the Kaplan-Meier/Nelson-Aalen estimator. Now does AFT regression too.

Calculates the empirical likelihood ratio and p-value for a mean-type hypothesis 
            (or multiple mean-type hypotheses) based on two samples with possible censored data.

Test hypotheses and construct confidence intervals for AUC (area under Receiver 
             Operating Characteristic curve) and pAUC (partial area under ROC curve), from the
			 given two samples of test data with disease/healthy subjects. The method used is
			 based on TWO SAMPLE empirical likelihood and PROFILE empirical likelihood, as described in
			 <https://www.ms.uky.edu/~mai/research/eAUC1.pdf>.

Compute the empirical likelihood ratio, -2LogLikRatio (Wilks) statistics,
             based on current status data for the hypothesis about the parameters of mean
			 or probability or weighted cumulative hazard.

Provides functions for computing test reliability and conditional
    standard error of measurement (CSEM) based on the methods described in
    the Reliability in Educational Measurement chapter of the 5th edition of 
    "Educational Measurement" by Lee and Harris (2025, ISBN:9780197654965).