#' MLwrap Comprehensive Tutorial
#'
#' A comprehensive tutorial demonstrating the complete MLwrap workflow is
#' available. The tutorial provides detailed guidance on data preprocessing,
#' model building, hyperparameter tuning, model evaluation, and sensitivity
#' analysis across all supported machine learning algorithms (Neural Networks,
#' Random Forests, SVM, and XGBoost) within the Knowledge Discovery in
#' Databases (KDD) framework.
#'
#' **Citation**: Jiménez, R., Martínez-García, J., Montaño, J. J., & Sesé, A.
#' (2025). *MLwrap: Simplifying Machine Learning workflows in R. PsyarXiv.*
#' \doi{10.31234/osf.io/j6m4z_v2}
#'
#' @section Preprint:
#' Available at \doi{10.31234/osf.io/j6m4z_v2}
#' @section Why consult the tutorial:
#' While MLwrap provides a streamlined and user-friendly interface for
#' implementing machine learning workflows, the underlying models represent
#' sophisticated algorithms with substantial theoretical and computational
#' complexity. The tutorial bridges this gap by explaining the rationale
#' behind preprocessing decisions, hyperparameter choices, and interpretation
#' of model outputs. Understanding these concepts ensures appropriate
#' application of the methods, proper interpretation of results, and awareness
#' of potential limitations in specific contexts.
#'
#' The tutorial demonstrates practical applications through complete workflows,
#' helping users navigate the balance between methodological rigor and
#' implementation simplicity that MLwrap offers. This is particularly valuable
#' for researchers transitioning from traditional statistical methods to
#' machine learning approaches, or those seeking to ensure reproducible and
#' theoretically sound applications in their work.
#'
#' Users are strongly encouraged to consult the tutorial for detailed examples
#' and best practices.
#' @section Tutorial for implementing ML with Python:
#' This paper is also interesting for ML users as it serves as a primer
#' for estimating ML models using Python code, particularly in the context of
#' Social, Health, and Behavioral research.
#'
#' Martínez-García, J., Montaño, J. J., Jiménez, R., Gervilla, E.,
#' Cajal, B., Núñez, A., Leguizamo, F., & Sesé, A. (2025).
#' Decoding Artificial Intelligence: A Tutorial on Neural Networks
#' in Behavioral Research. \emph{Clinical and Health, 36}(2), 77-95.
#' \doi{10.5093/clh2025a13}
#' @return Character string with the arXiv URL
#'
#' @name tutorial
#' @rdname tutorial
#' @export
#' @examples
#' MLwrap_tutorial()
#'
MLwrap_tutorial <- function() {
  "https://doi.org/10.31234/osf.io/j6m4z_v2"
}

