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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)
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)
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.
Import and Analyse Ego-Centered Network Data
Tools for importing, analyzing and visualizing ego-centered
network data. Supports several data formats, including the export formats of
'EgoNet', 'EgoWeb 2.0' and 'openeddi'. An interactive (shiny) app for the
intuitive visualization of ego-centered networks is provided. Also included
are procedures for creating and visualizing Clustered Graphs
(Lerner 2008
Network Analysis for Omics Data
Comprehensive network analysis package. Calculate correlation network fastly, accelerate lots of analysis by parallel computing. Support for multi-omics data, search sub-nets fluently. Handle bigger data, more than 10,000 nodes in each omics. Offer various layout method for multi-omics network and some interfaces to other software ('Gephi', 'Cytoscape', 'ggplot2'), easy to visualize. Provide comprehensive topology indexes calculation, including ecological network stability.
Modeling and Inferring Gene Networks
Analyzes gene expression (time series) data with focus on the inference of gene networks. In particular, GeneNet implements the methods of Schaefer and Strimmer (2005a,b,c) and Opgen-Rhein and Strimmer (2006, 2007) for learning large-scale gene association networks (including assignment of putative directions).
Biological and Chemical Data Networks
Data Package that includes several examples of chemical and biological data networks, i.e. data graph structured.
Network Dynamic Temporal Visualizations
Renders dynamic network data from 'networkDynamic' objects as movies, interactive animations, or other representations of changing relational structures and attributes.