Transmissions and Receptions in an End to End Network

Computes the expectation of the number of transmissions and receptions considering an End-to-End transport model with limited number of retransmissions per packet. It provides theoretical results and also estimated values based on Monte Carlo simulations. It is also possible to consider random data and ACK probabilities.

Transmissions and Receptions in a Hop by Hop Network

Computes the expectation of the number of transmissions and receptions considering a Hop-by-Hop transport model with limited number of retransmissions per packet. It provides the theoretical results shown in Palma et. al.(2016) and also estimated values based on Monte Carlo simulations. It is also possible to consider random data and ACK probabilities.

Network Analysis on the Norwegian Road Network

A collection of GIS (Geographic Information System) functions in R, created for use in Statistics Norway. The functions are primarily related to network analysis on the Norwegian road network.

Fit, Simulate and Diagnose Exponential-Family Models for Multiple or Multilayer Networks

A set of extensions for the 'ergm' package to fit multilayer/multiplex/multirelational networks and samples of multiple networks. 'ergm.multi' is a part of the Statnet suite of packages for network analysis. See Krivitsky, Koehly, and Marcum (2020) and Krivitsky, Coletti, and Hens (2023) .

Fit, Simulate and Diagnose Exponential-Family Models for Networks with Count Edges

A set of extensions for the 'ergm' package to fit weighted networks whose edge weights are counts. See Krivitsky (2012) and Krivitsky, Hunter, Morris, and Klumb (2023) .

Animal Social Network Inference and Permutations for Ecologists

Implements several tools that are used in animal social network analysis, as described in Whitehead (2007) Analyzing Animal Societies and Farine & Whitehead (2015) . In particular, this package provides the tools to infer groups and generate networks from observation data, perform permutation tests on the data, calculate lagged association rates, and performed multiple regression analysis on social network data.

Inferring Large-Scale Gene Networks with C3NET

Allows inferring gene regulatory networks with direct physical interactions from microarray expression data using C3NET.

Network Meta-Analysis Using Bayesian Methods

Network meta-analyses (mixed treatment comparisons) in the Bayesian framework using JAGS. Includes methods to assess heterogeneity and inconsistency, and a number of standard visualizations. van Valkenhoef et al. (2012) ; van Valkenhoef et al. (2015) .

Network Sparsification

Network sparsification with a variety of novel and known network sparsification techniques. All network sparsification techniques reduce the number of edges, not the number of nodes. Network sparsification is sometimes referred to as network dimensionality reduction. This package is based on the work of Spielman, D., Srivastava, N. (2009). Koutis I., Levin, A., Peng, R. (2013). Toivonen, H., Mahler, S., Zhou, F. (2010). Foti, N., Hughes, J., Rockmore, D. (2011).

Integration Network

It constructs a Consensus Network which identifies the general information of all the layers and Specific Networks for each layer with the information present only in that layer and not in all the others.The method is described in Policastro et al. (2024) "INet for network integration" .