Access the ArcGIS Routing and Network Analysis Services

Bindings to the ArcGIS Routing REST API (< https://developers.arcgis.com/rest/routing/>) for solving network analysis problems. Plan routes and generate driving directions, measure travel time and distance with origin-destination cost matrices, build service areas, find the closest facilities, route fleets of vehicles, and snap GPS tracks to roads. Both synchronous requests and asynchronous geoprocessing jobs are supported, returning simple features ('sf') objects ready for analysis and mapping.

Graph/Network Analysis Based on L1 Centrality

Analyze graph/network data using L1 centrality and prestige. Functions for deriving global, local, and group L1 centrality/prestige are provided. Routines for visual inspection of a graph/network are also provided. Details are in Kang and Oh (2026a) , Kang and Oh (2026b) , and Kang (2025) .

Structural Equation Modeling and Confirmatory Network Analysis

Multi-group (dynamical) structural equation models in combination with confirmatory network models from cross-sectional, time-series and panel data . Allows for confirmatory testing and fit as well as exploratory model search.

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.

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').

Sparse Multiple Canonical Correlation Network Analysis Tool ('SmCCNet')

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. Methods are described in Shi et al. (2019) "Unsupervised discovery of phenotype-specific multi-omics networks" .

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) . It also provides module analysis included PCA analysis, two enrichment analysis, Planner maximally filtered graph extraction and hub analysis.

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) .