Imports: arrangements, doParallel, dplyr, igraph, ggplot2, ggraph,
magrittr, patchwork, parallel, picante, rlang, Rfast, tibble,

Title: Estimates network indices, including trophic structure of foodwebs
Description: Given a network (e.g. a food web), estimates several network indices. These include: Ascendency network indices, Direct and indirect dependencies, Effective measures, Environ network indices, General network indices, Pathway analysis, Network uncertainty indices and constraint efficiencies and the trophic level and omnivory indices of food webs.
Packaged: Fri Jul 18 09:39:11 2008; hornik

Title: Estimating network indices, including trophic structure of foodwebs in R
Packaged: Fri Jan 16 14:39:59 2009; karlines

Title: Estimating network indices, including trophic structure of
Author: Karline Soetaert <k.soetaert@nioo.knaw.nl>, Julius Kipyegon
Kones <jkones@uonbi.ac.ke>, Dick van Oevelen
Description: Given a network (e.g. a food web), estimates several
network indices. These include: Ascendency network indices,
Direct and indirect dependencies, Effective measures, Environ
network indices, General network indices, Pathway analysis,
Network uncertainty indices and constraint efficiencies and the
trophic level and omnivory indices of food webs.

Author: Karline Soetaert <karline.soetaert@nioz.nl>, Julius Kipyegon

Title: Estimating Network Indices, Including Trophic Structure of

Title: Methods for Unweighted and Weighted Network Integration
person("Giorgio", "Valentini", email = "valentini@di.unimi.it", role = "aut"),
person("Jessica", "Gliozzo", email = "jessica.gliozzo@unimi.it", role = "cre"))
Description: 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).