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.

Geometrical Functionality of the 'spatstat' Family

Defines spatial data types and supports geometrical operations on them. Data types include point patterns, windows (domains), pixel images, line segment patterns, tessellations and hyperframes. Capabilities include creation and manipulation of data (using command line or graphical interaction), plotting, geometrical operations (rotation, shift, rescale, affine transformation), convex hull, discretisation and pixellation, Dirichlet tessellation, Delaunay triangulation, pairwise distances, nearest-neighbour distances, distance transform, morphological operations (erosion, dilation, closing, opening), quadrat counting, geometrical measurement, geometrical covariance, colour maps, calculus on spatial domains, Gaussian blur, level sets of images, transects of images, intersections between objects, minimum distance matching. (Excludes spatial data on a network, which are supported by the package 'spatstat.linnet'.)

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 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) .

Many Marks, Measures, Memberships, and Motifs for Networks

Many tools for calculating network, node, or tie marks, measures, motifs and memberships of many different types of networks. Marks identify structural positions, measures quantify network properties, memberships classify nodes into groups, and motifs tabulate substructure participation. All functions operate with all classes of network data covered in 'manynet', and on directed, undirected, multiplex, multimodal, signed, and other networks.

Algebraic Tools for the Analysis of Multiple Social Networks

Algebraic procedures for analyses of multiple social networks are provided with this package as described in Ostoic (2020) . 'multiplex' supports the creation and analysis of multiplex, multimode, and multilevel network data in various formats. It combines methods based on partially ordered semigroups, decomposition procedures, semiring structures, and relational bundles for multivariate network analysis. It also provides an algebraic approach to affiliation networks using Galois derivations, with visualization options.

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.

Extension to 'spatstat' for Large Datasets on a Linear Network

Extension to the 'spatstat' family of packages, for analysing large datasets of spatial points on a network. The geometrically- corrected K function is computed using a memory-efficient tree-based algorithm described by Rakshit, Baddeley and Nair (2019).

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" .