Implementation of network integration approaches
    comprising unweighted and weighted integration methods. Unweighted integration
    is performed considering the average, per-edge average, maximum and minimum of
    networks edges. Weighted integration takes into account a weight for each 
    network during the fusion process, where the weights express
    the ''predictiveness strength'' of each network considering a specific predictive
    task. Weights can be learned using a machine learning algorithm able to associate
    the weights to the assessment of the accuracy of the learning algorithm 
    trained on the network itself. The implemented methods can be applied to 
    effectively integrate different biological networks modelling a wide range
    of problems in bioinformatics (e.g. disease gene prioritization, protein
    function prediction, drug repurposing, clinical outcome prediction).

Designed to create interactive and visually compelling network maps using R Shiny. It allows users to quickly analyze CSV files and visualize complex relationships, structures, and connections within data by leveraging powerful network analysis libraries and dynamic web interfaces.

Build and run spatially explicit
    agent-based models using only the R platform. 'NetLogoR' follows the same
    framework as the 'NetLogo' software
    (Wilensky (1999) <https://www.netlogo.org>) and is a translation
    in R of the structure and functions of 'NetLogo'.
    'NetLogoR' provides new R classes to define model agents and functions to
    implement spatially explicit agent-based models in the R environment.
    This package allows benefiting of the fast and easy coding phase from the
    highly developed 'NetLogo' framework, coupled with the versatility, power
    and massive resources of the R software.
    Examples of two models from the NetLogo software repository 
    (Ants <https://ccl.northwestern.edu/netlogo/models/Ants>) and 
    Wolf-Sheep-Predation 
    (<https://ccl.northwestern.edu/netlogo/models/WolfSheepPredation>),
    and a third, Butterfly, from 
    Railsback and Grimm (2012) <https://www.railsback-grimm-abm-book.com/>, all
    written using 'NetLogoR' are available. 
    The 'NetLogo' code of the original version of these
    models is provided alongside.
    A programming guide inspired from the 'NetLogo' Programming Guide
    (<https://docs.netlogo.org/programming.html>) and a dictionary
    of 'NetLogo' primitives (<https://docs.netlogo.org/dictionary.html>)
    equivalences are also available.
    NOTE: To increment 'time', these functions can use a for loop or can be
    integrated with a discrete event simulator, such as 'SpaDES'
    (<https://cran.r-project.org/package=SpaDES>).