Additive and Multiplicative Effects Models for Networks and Relational Data

Analysis of dyadic network and relational data using additive and multiplicative effects (AME) models. The basic model includes regression terms, the covariance structure of the social relations model (Warner, Kenny and Stoto (1979) , Wong (1982) ), and multiplicative factor models (Hoff(2009) ). Several different link functions accommodate different relational data structures, including binary/network data, normal relational data, zero-inflated positive outcomes using a tobit model, ordinal relational data and data from fixed-rank nomination schemes. Several of these link functions are discussed in Hoff, Fosdick, Volfovsky and Stovel (2013) . Development of this software was supported in part by NIH grant R01HD067509.

Inferential Methods for Multimodal and Other Networks

A set of tools for testing networks. It includes functions for univariate and multivariate conditional uniform graph and quadratic assignment procedure testing, and network regression. The package is a complement to 'Multimodal Political Networks' (2021, ISBN:9781108985000), and includes various datasets used in the book. Built on the 'manynet' package, all functions operate with matrices, edge lists, and 'igraph', 'network', and 'tidygraph' objects, and on one-mode and two-mode (bipartite) networks.

Analysis and Mining of Multilayer Social Networks

Functions for the creation/generation and analysis of multilayer social networks .

Tools for Temporal Social Network Analysis

Temporal SNA tools for continuous- and discrete-time longitudinal networks having vertex, edge, and attribute dynamics stored in the 'networkDynamic' format. This work was supported by grant R01HD68395 from the National Institute of Health.

Bayesian Network Structure Learning from Data with Missing Values

Bayesian Network Structure Learning from Data with Missing Values. The package implements the Silander-Myllymaki complete search, the Max-Min Parents-and-Children, the Hill-Climbing, the Max-Min Hill-climbing heuristic searches, and the Structural Expectation-Maximization algorithm. Available scoring functions are BDeu, AIC, BIC. The package also implements methods for generating and using bootstrap samples, imputed data, inference.

Get Network Representation of an R Package

Tools from the domain of graph theory can be used to quantify the complexity and vulnerability to failure of a software package. That is the guiding philosophy of this package. 'pkgnet' provides tools to analyze the dependencies between functions in an R package and between its imported packages. See the pkgnet website for vignettes and other supplementary information.

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.

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.

Minimalist Async Evaluation Framework for R

Evaluates R expressions asynchronously and in parallel, locally or distributed across networks. An official parallel cluster type for R. Built on 'nanonext' and 'NNG', its non-polling, event-driven architecture scales from a laptop to thousands of processes across high-performance computing clusters and cloud platforms. Features FIFO scheduling with task cancellation and bounded queues, promises for reactive programming, 'OpenTelemetry' distributed tracing, and custom serialization for cross-language data types.

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'.)