Algorithms for robust multivariate statistics (STochAstic Robust multivaRiate Statistics) 
  including geometric median and geometric median covariance computation, 
  k-medians clustering and robust median Principal Compenents Analysis (PCA), 
  robust estimation of parameters for Gaussian, Student, or Laplace mixture models.
  'STARRS' provides an independent, clean, consolidated and cohesive framework,
  while drawing inspiration from the approaches implemented in packages 'Gmedian', 'Kmedians', 'RGMM', 'RobRegression'.
  Methods used in the package refer to 
  H. Robbins, S. Monro (1951) <doi:10.1214/aoms/1177729586>;
  D. Kraus, V. M. Panaretos (2012) <doi:10.1093/biomet/ass037>;
  H. Cardot, A. Godichon-Baggioni (2015) <doi:10.48550/arXiv.1504.02852>;
  A. Godichon-Baggioni, S. Robin (2024) <doi:10.1007/s11222-023-10362-9>.

Provides modular functions and applications for quickly generating 
    plots and tables. Each modular function opens a graphical user interface 
    providing the user with options to create and customise plots and tables.

Reading, manipulating, writing and plotting
    spatiotemporal arrays (raster and vector data cubes) in 'R', using 'GDAL'
    bindings provided by 'sf', and 'NetCDF' bindings by 'ncmeta' and 'RNetCDF'.

Data in multidimensional systems is obtained from operational
    systems and is transformed to adapt it to the new structure.
    Frequently, the operations to be performed aim to transform a flat
    table into a star schema. Transformations can be carried out using
    professional extract, transform and load tools or tools intended for
    data transformation for end users. With the tools mentioned, this
    transformation can be carried out, but it requires a lot of work. The
    main objective of this package is to define transformations that allow
    obtaining stars from flat tables easily. In addition, it includes
    basic data cleaning, dimension enrichment, incremental data refresh
    and query operations, adapted to this context.