Minimalist Async Evaluation Framework for R

Designed for simplicity, a 'mirai' evaluates an R expression asynchronously, locally or distributed over the network. Built on 'nanonext' and 'NNG' for modern networking and concurrency, scales efficiently to millions of tasks over thousands of parallel processes. Provides optimal scheduling over fast 'IPC', TCP, and TLS connections, integrating with SSH or cluster managers. Implements event-driven promises for reactive programming, and supports custom serialization for cross-language data types.

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) .

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'.)

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 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" .

Clustering for networks

Facilitates network clustering and evaluation of cluster configurations.