With 'satin' functions, visualisation, data extraction and further analysis like producing climatologies from several images, and anomalies of satellite derived ocean data can be easily done.  Reading functions can import a user defined geographical extent of data stored in netCDF files.  Currently supported ocean data sources include NASA's Oceancolor web page <https://oceancolor.gsfc.nasa.gov/>, sensors VIIRS-SNPP; MODIS-Terra; MODIS-Aqua; and SeaWiFS.  Available variables from this source includes chlorophyll concentration, sea surface temperature (SST), and several others.  Data sources specific for SST that can be imported too includes Pathfinder AVHRR <https://www.ncei.noaa.gov/products/avhrr-pathfinder-sst> and GHRSST <https://www.ghrsst.org/>.  In addition, ocean productivity data produced by Oregon State University <http://sites.science.oregonstate.edu/ocean.productivity/> can also be handled previous conversion from HDF4 to HDF5 format.  Many other ocean variables can be processed by importing netCDF data files from two European Union's Copernicus Marine Service databases <https://marine.copernicus.eu/>, namely Global Ocean Physical Reanalysis and Global Ocean Biogeochemistry Hindcast.

Given raster files directly downloaded from various websites,
    it generates a raster structure where it merges them if they are tiles
    of the same scene and classifies them according to their spectral and
    spatial resolution for easy access by name.

Bayesian inference of graphical model structures using spanning trees.

Machine learning provides algorithms that can learn from data and make inferences or predictions. Stochastic automata is a class of input/output devices which can model components. This work provides implementation an inference algorithm for stochastic automata which is similar to the Viterbi algorithm. Moreover, we specify a learning algorithm using the expectation-maximization technique and provide a more efficient implementation of the Baum-Welch algorithm for stochastic automata. This work is based on Inference and learning in stochastic automata was by Karl-Heinz Zimmermann(2017) <doi:10.12732/ijpam.v115i3.15>.