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Spatial Watershed Aggregation and Spatial Drainage Network Analysis
Methods for watersheds aggregation and spatial drainage network analysis.
Sparse Multiple Canonical Correlation Network Analysis Tool
A canonical correlation based framework (SmCCNet) designed for the construction of phenotype-specific multi-omics networks. This framework adeptly integrates single or multiple omics data types along with a quantitative or binary phenotype of interest. It offers a streamlined setup process that can be tailored manually or configured automatically, ensuring a flexible and user-friendly experience.
Pseudo-Value Regression Approach for Network Analysis (PRANA)
A novel pseudo-value regression approach for the differential co-expression network analysis in expression data, which can incorporate additional clinical variables in the model. This is a direct regression modeling for the differential network analysis, and it is therefore computationally amenable for the most users. The full methodological details can be found in Ahn S et al (2023)
Software Tools for the Statistical Analysis of Network Data
Statnet is a collection of packages for statistical network analysis that are designed to work together because they share common data representations and 'API' design. They provide an integrated set of tools for the representation, visualization, analysis, and simulation of many different forms of network data. This package is designed to make it easy to install and load the key 'statnet' packages in a single step. Learn more about 'statnet' at < http://www.statnet.org>. Tutorials for many packages can be found at < https://github.com/statnet/Workshops/wiki>. For an introduction to functions in this package, type help(package='statnet').
Disease-Drived Differential Proteins Co-Expression Network Analysis
Functions designed to connect disease-related differential proteins and
co-expression network. It provides the basic statics analysis included t test, ANOVA analysis.
The network construction is not offered by the package, you can used 'WGCNA' package which you
can learn in Peter et al. (2008)
Network Analysis and Causal Inference Through Structural Equation Modeling
Estimate networks and causal relationships in complex systems through
Structural Equation Modeling. This package also includes functions for importing,
weight, manipulate, and fit biological network models within the
Structural Equation Modeling framework as outlined in the Supplementary Material of
Grassi M, Palluzzi F, Tarantino B (2022)
Fit, Simulate and Diagnose Exponential-Family Models for Networks
An integrated set of tools to analyze and simulate networks based on exponential-family random graph models (ERGMs). 'ergm' is a part of the Statnet suite of packages for network analysis. See Hunter, Handcock, Butts, Goodreau, and Morris (2008)
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
Data Analysis for IP Addresses and Networks
Classes and functions for working with IP (Internet Protocol) addresses and networks, inspired by the Python 'ipaddress' module. Offers full support for both IPv4 and IPv6 (Internet Protocol versions 4 and 6) address spaces. It is specifically designed to work well with the 'tidyverse'.
Graph/Network Visualization
Build graph/network structures using functions for stepwise addition and deletion of nodes and edges. Work with data available in tables for bulk addition of nodes, edges, and associated metadata. Use graph selections and traversals to apply changes to specific nodes or edges. A wide selection of graph algorithms allow for the analysis of graphs. Visualize the graphs and take advantage of any aesthetic properties assigned to nodes and edges.