Estimates the multi-level vector autoregression model on time-series data.
             Three network structures are obtained: temporal networks, contemporaneous
             networks and between-subjects networks.

A minimal library specifically designed to make the estimation of
             Machine Learning (ML) techniques as easy and accessible as possible,
             particularly within the framework of the Knowledge Discovery in
             Databases (KDD) process in data mining. The package provides
             essential tools to structure and execute each stage of a predictive
             or classification modeling workflow, aligning closely with the
             fundamental steps of the KDD methodology, from data selection and
             preparation, through model building and tuning, to the
             interpretation and evaluation of results using Sensitivity Analysis.
             The 'MLwrap' workflow is organized into four core steps;
             preprocessing(), build_model(), fine_tuning(), and
             sensitivity_analysis(). These steps correspond, respectively, to
             data preparation and transformation, model construction,
             hyperparameter optimization, and sensitivity analysis. The user can
             access comprehensive model evaluation results including fit
             assessment metrics, plots, predictions, and performance diagnostics
             for ML models implemented through 'Neural Networks', 'Random Forest',
             'XGBoost' (Extreme Gradient Boosting), and 'Support Vector Machines'
             (SVM) algorithms. By streamlining these phases, 'MLwrap' aims to
             simplify the implementation of ML techniques, allowing analysts and
             data scientists to focus on extracting actionable insights and
             meaningful patterns from large datasets, in line with the objectives
             of the KDD process.

Simulation and visualization of complex
    models for longitudinal data. The models are encoded using the model coding
    language 'Mlxtran' and automatically converted into C++ codes. 
    That allows one to implement very easily complex ODE-based models and complex 
    statistical models, including mixed effects models, for continuous, count, 
    categorical, and time-to-event data.