A cross-validated minimal-optimal feature selection algorithm.
 It utilises popularity counting, hierarchical clustering with feature dissimilarity measures,
 and prefiltering with all-relevant feature selection method to obtain the minimal-optimal set of features.

Ranking of Alternatives through Functional mapping of criterion sub-intervals into a Single Interval Method is designed to perform multi-criteria decision-making (MCDM), developed by Mališa Žižovic in 2020 (<doi:10.3390/math8061015>). 
              It calculates the final sorted rankings based on a decision matrix where rows represent alternatives 
              and columns represent criteria. The method uses:
              - A numeric vector of weights for each criterion (the sum of weights must be 1).
              - A numeric vector of ideal values for each criterion.
              - A numeric vector of anti-ideal values for each criterion.
              - Numeric values representing the extent to which the ideal value is preferred over the anti-ideal value, 
                and the extent to which the anti-ideal value is considered worse.
              The function standardizes the decision matrix, normalizes the data, applies weights, and returns the 
              final sorted rankings.

Functions for calculating life history metrics using matrix
    population models ('MPMs'). Described in Jones et al. (2021)
    <doi:10.1101/2021.04.26.441330>.

Anti-Grain Geometry (AGG) is a high-quality and
    high-performance 2D drawing library. The 'ragg' package provides a set
    of graphic devices based on AGG to use as alternative to the raster
    devices provided through the 'grDevices' package.

Data objects in 'R' can be rendered as 'HTML' tables using the
    'JavaScript' library 'ag-grid' (typically via 'R Markdown' or 'Shiny'). The
    'ag-grid' library has been included in this 'R' package. The package name
    'RagGrid' is an abbreviation of 'R agGrid'.

Proper L2-penalized maximum likelihood estimators for precision 
  matrices and supporting functions to employ these estimators in a graphical 
  modeling setting. For details, see Peeters, Bilgrau, & van Wieringen (2022) 
  <doi:10.18637/jss.v102.i04> and associated publications.