Statistical Network Models for Dynamic Network Data

Tools for fitting statistical network models to dynamic network data. Can be used for fitting both dynamic network actor models ('DyNAMs') and relational event models ('REMs'). Stadtfeld, Hollway, and Block (2017a) , Stadtfeld, Hollway, and Block (2017b) , Stadtfeld and Block (2017) , Hoffman et al. (2020) .

Random Network Model Estimation, Selection and Parameter Tuning

Model fitting, model selection and parameter tuning procedures for a class of random network models. Many useful network modeling, estimation, and processing methods are included. The work to build and improve this package is partially supported by the NSF grants DMS-2015298 and DMS-2015134.

Parallel Mutual Information Estimation for Gene Network Reconstruction

Parallel estimation of the mutual information based on entropy estimates from k-nearest neighbors distances and algorithms for the reconstruction of gene regulatory networks (Sales et al, 2011 ).

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.