Statistical Network Analysis of Animal Social Networks

Obtain network structures from animal GPS telemetry observations and statistically analyse them to assess their adequacy for social network analysis. Methods include pre-network data permutations, bootstrapping techniques to obtain confidence intervals for global and node-level network metrics, and correlation and regression analysis of the local network metrics.

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) .

Tools for Generating, Visualizing, and Analysing Link Communities in Networks

Link communities reveal the nested and overlapping structure in networks, and uncover the key nodes that form connections to multiple communities. linkcomm provides a set of tools for generating, visualizing, and analysing link communities in networks of arbitrary size and type. The linkcomm package also includes tools for generating, visualizing, and analysing Overlapping Cluster Generator (OCG) communities. Kalinka and Tomancak (2011) .

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 ).

Random Network Model Estimation, Selection and Parameter Tuning

Model selection and parameter tuning procedures for a class of random network models. The model selection can be done by a general cross-validation framework called ECV from Li et. al. (2016) . Several other model-based and task-specific methods are also included, such as NCV from Chen and Lei (2016) , likelihood ratio method from Wang and Bickel (2015) , spectral methods from Le and Levina (2015) . Many network analysis methods are also implemented, such as the regularized spectral clustering (Amini et. al. 2013 ) and its degree corrected version and graphon neighborhood smoothing (Zhang et. al. 2015 ). It also includes the consensus clustering of Gao et. al. (2014) , the method of moments estimation of nomination SBM of Li et. al. (2020) , and the network mixing method of Li and Le (2021) . It also includes the informative core-periphery data processing method of Miao and Li (2021) . The work to build and improve this package is partially supported by the NSF grants DMS-2015298 and DMS-2015134.

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.

Visualising Bipartite Networks and Calculating Some (Ecological) Indices

Functions to visualise webs and calculate a series of indices commonly used to describe pattern in (ecological) webs. It focuses on webs consisting of only two levels (bipartite), e.g. pollination webs or predator-prey-webs. Visualisation is important to get an idea of what we are actually looking at, while the indices summarise different aspects of the web's topology.

Network Maze Generator

A network Maze generator that creates different types of network mazes.