Implementation of SAPEVO-M, a Group Ordinal Method for Multiple Criteria Decision-Making (MCDM). SAPEVO-M is an acronym for Simple Aggregation of Preferences Expressed by Ordinal Vectors Group Decision Making. This method provides alternatives ranking given decision makers' preferences: criteria preferences and alternatives preferences for each criterion.This method is described in Gomes et al. (2020) <doi: 10.1590/0101-7438.2020.040.00226524 >.

Access, modify, aggregate and plot data from the 'Sapfluxnet' project,
  the first global database of sap flow measurements.

In ecology, spatial data is often represented using polygons. These
  polygons can represent a variety of spatial entities, such as ecological
  patches, animal home ranges, or gaps in the forest canopy.  Researchers often
  need to determine if two spatial processes, represented by these polygons, are
  independent of each other. For instance, they might want to test if the home
  range of a particular animal species is influenced by the presence of a
  certain type of vegetation.  To address this, Godoy et al. (2022)
  (<doi:10.1016/j.spasta.2022.100695>) developed conditional Monte Carlo
  tests. These tests are designed to assess spatial independence while taking
  into account the shape and size of the polygons.

Functions for statistical analysis of point processes.

A collection of tools to access prepared air quality monitoring
    data files from web servers with ease and speed. Air quality data are 
    sourced from open and publicly accessible repositories and can be found in 
    these locations: 
    <https://www.eea.europa.eu/data-and-maps/data/airbase-the-european-air-quality-database-8> 
    and <https://discomap.eea.europa.eu/map/fme/AirQualityExportAirbase.htm>. 
    The web server space has been provided by Ricardo Energy & Environment.

'Smart Adaptive Recommendations' (SAR) is the name of a fast, scalable, adaptive algorithm for personalized recommendations based on user transactions and item descriptions. It produces easily explainable/interpretable recommendations and handles "cold item" and "semi-cold user" scenarios. This package provides two implementations of 'SAR': a standalone implementation, and an interface to a web service in Microsoft's 'Azure' cloud: <https://github.com/Microsoft/Product-Recommendations/blob/master/doc/sar.md>. The former allows fast and easy experimentation, and the latter provides robust scalability and extra features for production use.