Software Tools to Quantify Structural Importance of Nodes in a Network

Provides functionality to compute various node centrality measures on networks. Included are functions to compute betweenness centrality (by utilizing Madduri and Bader's SNAP library), implementations of constraint and effective network size by Burt (2000) ; algorithm to identify key players by Borgatti (2006) ; and the bridging algorithm by Valente and Fujimoto (2010) . On Unix systems, the betweenness, Key Players, and bridging implementations are parallelized with OpenMP, which may run faster on systems which have OpenMP configured.

Additive and Multiplicative Effects Models for Networks and Relational Data

Analysis of dyadic network and relational data using additive and multiplicative effects (AME) models. The basic model includes regression terms, the covariance structure of the social relations model (Warner, Kenny and Stoto (1979) , Wong (1982) ), and multiplicative factor models (Hoff(2009) ). Several different link functions accommodate different relational data structures, including binary/network data, normal relational data, zero-inflated positive outcomes using a tobit model, ordinal relational data and data from fixed-rank nomination schemes. Several of these link functions are discussed in Hoff, Fosdick, Volfovsky and Stovel (2013) . Development of this software was supported in part by NIH grant R01HD067509.

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) ; Okabe et al (2012, ISBN:978-0470770818);Baddeley et al (2015, ISBN:9781482210200).

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>).