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Retrieve Network Statistics Including Available TCP Ports
R interface for the 'netstat' command line utility used to retrieve and parse commonly used network statistics, including available and in-use transmission control protocol (TCP) ports. Primers offering technical background information on the 'netstat' command line utility are available in the "Linux System Administrator's Manual" by Michael Kerrisk (2014) < https://man7.org/linux/man-pages/man8/netstat.8.html>, and on the Microsoft website (2017) < https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/netstat>.
Client for the Comprehensive Knowledge Archive Network ('CKAN') API
Client for 'CKAN' API (< https://ckan.org/>). Includes interface to 'CKAN' 'APIs' for search, list, show for packages, organizations, and resources. In addition, provides an interface to the 'datastore' API.
Routing Distribution, Broadcasts, Transmissions and Receptions in an Opportunistic Network
Computes the routing distribution, the expectation of the number of broadcasts, transmissions and receptions considering an Opportunistic transport model. It provides theoretical results and also estimated values based on Monte Carlo simulations.
Network Maze Generator
A network Maze generator that creates different types of network mazes.
Weighted and Directed Networks
Assortativity coefficients, centrality measures, and clustering coefficients for weighted and directed networks. Rewiring unweighted networks with given assortativity coefficients. Generating general preferential attachment networks.
Spatial Analysis on Network
Perform spatial analysis on network.
Implement several methods for spatial analysis on network: Network Kernel Density estimation,
building of spatial matrices based on network distance ('listw' objects from 'spdep' package), K functions estimation
for point pattern analysis on network, k nearest neighbours on network, reachable area calculation, and graph generation
References: Okabe et al (2019)
Network-Based Clustering
Network-based clustering using a Bayesian network mixture model with optional covariate adjustment.
Personalized Disease Network
Building patient level networks for prediction of medical outcomes and draw the cluster of network. This package is based on paper Personalized disease networks for understanding and predicting cardiovascular diseases and other complex processes (See Cabrera et al. < http://circ.ahajournals.org/content/134/Suppl_1/A14957>).
Generative Neural Networks
Tools to set up, train, store, load, investigate and analyze generative neural networks. In particular, functionality for generative moment matching networks is provided.
'Gephi' Network Visualization
Implements key features of 'Gephi' for network visualization, including 'ForceAtlas2' (with LinLog mode), network scaling, and network rotations. It also includes easy network visualization tools such as edge and node color assignment for recreating 'Gephi'-style graphs in R. The package references layout algorithms developed by Jacomy, M., Venturini T., Heymann S., and Bastian M. (2014)