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Dynamic Extensions for Network Objects
Simple interface routines to facilitate the handling of network objects with complex intertemporal data. This is a part of the "statnet" suite of packages for network analysis.
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)
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)
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'.
A Shiny Application for End-to-End Bayesian Decision Network Analysis and Web-Deployment
A Shiny application for learning Bayesian Decision Networks from data. This package can be used for probabilistic reasoning (in the observational setting), causal inference (in the presence of interventions) and learning policy decisions (in Decision Network setting). Functionalities include end-to-end implementations for data-preprocessing, structure-learning, exact inference, approximate inference, extending the learned structure to Decision Networks and policy optimization using statistically rigorous methods such as bootstraps, resampling, ensemble-averaging and cross-validation. In addition to Bayesian Decision Networks, it also features correlation networks, community-detection, graph visualizations, graph exports and web-deployment of the learned models as Shiny dashboards.
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.
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