Implements the Capability-Ecological Developmental Model
    (CEDM) for longitudinal and multilevel data. The package supports
    estimation and interpretation of models examining how socioeconomic
    status (SES), health indicators, and contextual factors jointly relate
    to academic outcomes. Functionality includes: (1) classification of
    ecological capability regimes (amplifying, neutral, compensatory);
    (2) estimation of moderated multilevel models with higher-order
    interaction terms; (3) causal mediation analysis using doubly robust
    estimation; (4) random-effects within-between (REWB) decomposition;
    (5) nonlinear moderation using restricted cubic splines; (6) clustering
    of longitudinal health trajectories; and (7) sensitivity analysis using
    the impact threshold for a confounding variable (ITCV) and
    robustness-to-replacement (RIR) measures. The package is designed for
    use with general longitudinal multilevel datasets.

Noise in the time-series data significantly affects the accuracy of the Machine Learning (ML) models (Artificial Neural Network and Support Vector Regression are considered here). Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) decomposes the time series data into sub-series and help to improve the model performance. The models can achieve higher prediction accuracy than the traditional ML models. Two models have been provided here for time series forecasting. More information may be obtained from Garai and Paul (2023) <doi:10.1016/j.iswa.2023.200202>.

Model building, surrogate model
    based optimization and Efficient Global Optimization in combinatorial
    or mixed search spaces.

Contains a number of common astronomy utility functions for cosmology and angular coordinates.

Pool cells together before running differentially expression (DE) analysis. 
 Tell 'CellDEEP' how many cells you want to pool together (which shall be determined 
 by the overall cell number of data), then run DE analysis.
 Cheng et al. (2026) <doi:10.64898/2026.03.09.710522>.