Bootstrap Methods for Various Network Estimation Routines

Bootstrap methods to assess accuracy and stability of estimated network structures and centrality indices . Allows for flexible specification of any undirected network estimation procedure in R, and offers default sets for various estimation routines.

Interactive 3D Scatter Plots, Networks and Globes

Create interactive 3D scatter plots, network plots, and globes using the 'three.js' visualization library (< https://threejs.org>).

Software Tools for the Statistical Analysis of Network Data

Statnet is a collection of packages for statistical network analysis that are designed to work together because they share common data representations and 'API' design. They provide an integrated set of tools for the representation, visualization, analysis, and simulation of many different forms of network data. This package is designed to make it easy to install and load the key 'statnet' packages in a single step. Learn more about 'statnet' at < http://www.statnet.org>. Tutorials for many packages can be found at < https://github.com/statnet/Workshops/wiki>. For an introduction to functions in this package, type help(package='statnet').

Graph Plotting Methods, Psychometric Data Visualization and Graphical Model Estimation

Fork of qgraph - Weighted network visualization and analysis, as well as Gaussian graphical model computation. See Epskamp et al. (2012) .

Generalized Multipartite Networks

We define generalized multipartite networks as the joint observation of several networks implying some common pre-specified groups of individuals. The aim is to fit an adapted version of the popular stochastic block model to multipartite networks, as described in Bar-hen, Barbillon and Donnet (2020) .

Optimal Channel Networks

Generate and analyze Optimal Channel Networks (OCNs): oriented spanning trees reproducing all scaling features characteristic of real, natural river networks. As such, they can be used in a variety of numerical experiments in the fields of hydrology, ecology and epidemiology. See Carraro et al. (2020) for a presentation of the package; Rinaldo et al. (2014) for a theoretical overview on the OCN concept; Furrer and Sain (2010) for the construct used.

Clustering on Network of Samples

Wires together large collections of single-cell RNA-seq datasets, which allows for both the identification of recurrent cell clusters and the propagation of information between datasets in multi-sample or atlas-scale collections. 'Conos' focuses on the uniform mapping of homologous cell types across heterogeneous sample collections. For instance, users could investigate a collection of dozens of peripheral blood samples from cancer patients combined with dozens of controls, which perhaps includes samples of a related tissue such as lymph nodes. This package interacts with data available through the 'conosPanel' package, which is available in a 'drat' repository. To access this data package, see the instructions at < https://github.com/kharchenkolab/conos>. The size of the 'conosPanel' package is approximately 12 MB.

Algebraic Tools for the Analysis of Multiple Social Networks

Algebraic procedures for analyses of multiple social networks are delivered with this package as described in Ostoic (2020) . 'multiplex' makes possible, among other things, to create and manipulate multiplex, multimode, and multilevel network data with different formats. Effective ways are available to treat multiple networks with routines that combine algebraic systems like the partially ordered semigroup with decomposition procedures or semiring structures with the relational bundles occurring in different types of multivariate networks. 'multiplex' provides also an algebraic approach for affiliation networks through Galois derivations between families of the pairs of subsets in the two domains of the network with visualization options.

Weighted, Two-Mode, and Longitudinal Networks Analysis

Binary ties limit the richness of network analyses as relations are unique. The two-mode structure contains a number of features lost when projection it to a one-mode network. Longitudinal datasets allow for an understanding of the causal relationship among ties, which is not the case in cross-sectional datasets as ties are dependent upon each other.

Fit, Simulate and Diagnose Models for Network Evolution Based on Exponential-Family Random Graph Models

An integrated set of extensions to the 'ergm' package to analyze and simulate network evolution based on exponential-family random graph models (ERGM). 'tergm' is a part of the 'statnet' suite of packages for network analysis. See Krivitsky and Handcock (2014) and Carnegie, Krivitsky, Hunter, and Goodreau (2015) .