statnet: Software Tools for the Representation, Visualization, Analysis and Simulation of Network Data.

By Mark S. Handcock, David R. Hunter, Carter T. Butts, Steven M. Goodreau, Martina Morris. Journal of Statistical Software, 2008

862 citations recorded by the source corpus.

statnet is a suite of software packages for statistical network analysis. The packages implement recent advances in network modeling based on exponential-family random graph models (ERGM). The components of the package provide a comprehensive framework for ERGM-based network modeling, including tools for model estimation, model evaluation, model-based network simulation, and network visualization. This broad functionality is powered by a central Markov chain Monte Carlo (MCMC) algorithm. The coding is optimized for speed and robustness.

Subfields: Experimental and Cognitive Psychology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/09a43067053276e0b03977b87a6c1fc26d1862d2

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Citations in the corpus

Structural balance in the social networks of a wild mammal (2013, background): “The aim of ERGM is to predict the joint probability that a set of edges exists on nodes in a network and describe the local selection forces that shape the global structure of the network (Handcock et al. 2008).”
A guide to null models for animal social network analysis (2017, methodology): “Using the simulation framework previously described, I generated networks and fit a standard ERGMmodel (using the R package STATNET, Handcock et al. 2015) that includes sex as a node effect.”
A Survey of Statistical Network Models (2009, methodology): “Software is available to sample from the space of random graphs with a given degree distribution based on Monte Carlo Markov chain methods [42; 138].”
Qgraph: Network visualizations of relationships in psychometric data (2012, neutral): “A number of R packages can be used for the visualization and analysis of networks, e.g., network (Butts et al. 2012; Butts 2008a), statnet (Handcock et al. 2008), igraph (Csardi and Nepusz 2006).”
The effect of differential recruitment, non-response and non-recruitment on estimators for respondent-driven sampling (2010, methodology): “Networks were simulated using the R package statnet (Handcock et al., 2003, 2008).”
NetCoMi: network construction and comparison for microbiome data in R (2020, background): “Although a wide range of general-purpose tools is available for visualizing and analysing networks [22, 23], including the R packages igraph [24], statnet [25] and network [26], the special nature of current MGS data requires careful statistical treatment.”
A statnet Tutorial. (2008, methodology): “These models, also known as p* (p-star), are described in great detail in earlier papers in this issue (Handcock et al. 2008; Hunter et al. 2008b), and elsewhere in the literature (Wasserman and Pattison 1996; Snijders, Pattison, Robins, and Handcock 2006; Robins and Morris 2007).”
ergm: A Package to Fit, Simulate and Diagnose Exponential-Family Models for Networks. (2008, methodology): “In particular, the package allows users to obtain approximate (or, in some cases, exact) maximum likelihood estimates (MLEs); simulate random networks from a speciﬁed ERGM; and perform graphical goodness-of-ﬁt checks of the type described by Hunter, Goodreau, and Handcock (2008).”