Fit multilevel manifest or latent time-series models, including popular Dynamic Structural Equation Models (DSEM).
  The models can be set up and modified with user-friendly functions and are fit to the data using 'Stan' for Bayesian inference.
  Path models and formulas for user-defined models can be easily created with functions using 'knitr'. 
  Asparouhov, Hamaker, & Muthen (2018) <doi:10.1080/10705511.2017.1406803>.

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(). It also includes global and pairwise interaction analysis based on
             Friedman’s H-statistic to support a more detailed interpretation of complex feature
             relationships.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.