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Network Analysis and Visualization
Routines for simple graphs and network analysis. It can handle large graphs very well and provides functions for generating random and regular graphs, graph visualization, centrality methods and much more.
Tools for Social Network Analysis
A range of tools for social network analysis, including node and graph-level indices, structural distance and covariance methods, structural equivalence detection, network regression, random graph generation, and 2D/3D network visualization.
Methods and Measures for Brain, Cognitive, and Psychometric Network Analysis
Implements network analysis and graph theory measures used in neuroscience, cognitive science, and psychology. Methods include various filtering methods and approaches such as threshold, dependency (Kenett, Tumminello, Madi, Gur-Gershgoren, Mantegna, & Ben-Jacob, 2010
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.
Weighted Correlation Network Analysis
Functions necessary to perform Weighted Correlation Network Analysis on high-dimensional data as originally described in Horvath and Zhang (2005)
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.
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),
Mobility Network Analysis
Implements the method to analyse weighted mobility networks or distribution networks as outlined in:
Block, P., Stadtfeld, C., & Robins, G. (2022)
Multiplex Network Analysis
Interactions between different biological entities are crucial for the function of biological systems.
In such networks, nodes represent biological elements, such as genes, proteins and microbes, and their interactions can be defined by edges, which can be either binary or weighted.
The dysregulation of these networks can be associated with different clinical conditions such as diseases and response to treatments.
However, such variations often occur locally and do not concern the whole network.
To capture local variations of such networks, we propose multiplex network differential analysis (MNDA).
MNDA allows to quantify the variations in the local neighborhood of each node (e.g. gene) between the two given clinical states, and to test for statistical significance of such variation.
Yousefi et al. (2023)
Network Analysis and Visualization
Collection of functions for fast manipulation, handling, and analysis of large-scale networks based on family and social data. Functions are utility functions used to manipulate data in three "formats": sparse adjacency matrices, pedigree trio family data, and pedigree family data. When possible, the functions should be able to handle millions of data points quickly for use in combination with data from large public national registers and databases. Kenneth Lange (2003, ISBN:978-8181281135).