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Construction, Simulation and Analysis of Boolean Networks
Functions to reconstruct, generate, and simulate synchronous, asynchronous, probabilistic, and temporal Boolean networks. Provides also functions to analyze and visualize attractors in Boolean networks
An Simplified Implementation of the 'network' Package Functionality
An implementation of some of the core 'network' package functionality based on a simplified data structure that is faster in many research applications. This package is designed for back-end use in the 'statnet' family of packages, including 'EpiModel'. Support is provided for binary and weighted, directed and undirected, bipartite and unipartite networks; no current support for multigraphs, hypergraphs, or loops.
Fault Tolerant Simple Network of Workstations
Extension of the snow package supporting fault tolerant and reproducible applications, as well as supporting easy-to-use parallel programming - only one function is needed. Dynamic cluster size is also available.
Phylogenetic Reconstruction and Analysis
Allows for estimation of phylogenetic trees and networks using Maximum Likelihood, Maximum Parsimony, distance methods and Hadamard conjugation (Schliep 2011). Offers methods for tree comparison, model selection and visualization of phylogenetic networks as described in Schliep et al. (2017).
Spatial Modeling on Stream Networks
Spatial statistical modeling and prediction for data on stream networks, including models based on in-stream distance (Ver Hoef, J.M. and Peterson, E.E., (2010)
Bootstrap Methods for Various Network Estimation Routines
Bootstrap methods to assess accuracy and stability of estimated network structures
and centrality indices
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>).
Siena - Simulation Investigation for Empirical Network Analysis
The main purpose of this package is to perform simulation-based
estimation of stochastic actor-oriented models for longitudinal network
data collected as panel data. Dependent variables can be single or
multivariate networks, which can be directed, non-directed, or two-mode;
and associated actor variables.
There are also functions for testing parameters and checking goodness of fit.
An overview of these models is given in Snijders (2017),
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)