Statistical functions to identify, estimate and diagnose a Space-Time AutoRegressive Moving Average (STARMA) model.

Implements stacked elastic net regression (Rauschenberger 2020, <doi:10.1093/bioinformatics/btaa535>). The elastic net generalises ridge and lasso regularisation (Zou 2005, <doi:10.1111/j.1467-9868.2005.00503.x>). Instead of fixing or tuning the mixing parameter alpha, we combine multiple alpha by stacked generalisation (Wolpert 1992 <doi:10.1016/S0893-6080(05)80023-1>).

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.

Miscellaneous functions for working with 'stars' objects, mainly single-band rasters. Currently includes functions for: (1) focal filtering, (2) detrending of Digital Elevation Models, (3) calculating flow length, (4) calculating the Convergence Index, (5) calculating topographic aspect and topographic slope.

Makes it possible to serve map tiles for web maps (e.g. leaflet) based on a function or a stars object without having to render them in advance. This enables parallelization of the rendering, separating the data source and visualization location and to provide web services.