Converts R scripts (.R) into 'Quarto' markdown documents (.qmd) with
    automatic formatting. Recognizes 'RStudio' code sections, preserves comments as
    narrative text, extracts metadata from special comments, and provides both
    programmatic functions and an interactive 'RStudio' add-in for easy conversion.

Convert R Markdown documents and 'Jupyter' notebooks to a
    variety of output formats using 'Quarto'.

Provides helper functions to work programmatically within a quarto document. It allows 
    the user to create section headers, tabsets, divs, and spans, and formats these objects
    into quarto syntax when printed into a document.

The approach is based on the closed testing procedure to control familywise error rate in a strong sense.
  The local tests implemented are Wald-type and rank-score. 
  The method is described in De Santis, et al., (2026), <doi:10.48550/arXiv.2511.07999>.

Functions for estimating the potential dispersal of tree species 
  using regeneration densities and dispersal distances to nearest seed trees. 
  A quantile regression is implemented to determine the dispersal potential.
  Spatial prediction can be used to identify natural regeneration potential for forest restoration as described in Axer et al (2021) <doi:10.1016/j.foreco.2020.118802>.

Various quantile-based clustering algorithms: algorithm CU (Common theta and Unscaled variables), algorithm CS (Common theta and Scaled variables through lambda_j), algorithm VU (Variable-wise theta_j and Unscaled variables) and algorithm VW (Variable-wise theta_j and Scaled variables through lambda_j). Hennig, C., Viroli, C., Anderlucci, L. (2019) "Quantile-based clustering." Electronic Journal of Statistics. 13 (2) 4849 - 4883  <doi:10.1214/19-EJS1640>.

Syntax for defining complex filtering expressions in a programmatic way.
  A filtering query, built as a nested list configuration, can be easily stored in other formats like 'YAML' or 'JSON'.
  What's more, it's possible to convert such configuration to a valid expression that can be applied to 
  popular 'dplyr' package operations.