Visualization and Analysis Tools for Neural Networks

Visualization and analysis tools to aid in the interpretation of neural network models. Functions are available for plotting, quantifying variable importance, conducting a sensitivity analysis, and obtaining a simple list of model weights.

Deciphering Central Informative Nodes in Network Analysis

Computing, comparing, and demonstrating top informative centrality measures within a network. "CINNA: an R/CRAN package to decipher Central Informative Nodes in Network Analysis" provides a comprehensive overview of the package functionality Ashtiani et al. (2018) .

Differential Network Analysis using Gene Pathways

Integrates pathway information into the differential network analysis of two gene expression datasets as described in Grimes, Potter, and Datta (2019) . Provides summary functions to break down the results at the pathway, gene, or individual connection level. The differential networks for each pathway of interest can be plotted, and the visualization will highlight any differentially expressed genes and all of the gene-gene associations that are significantly differentially connected.

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) .

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.

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 ), Information Filtering Networks (Barfuss, Massara, Di Matteo, & Aste, 2016 ), and Efficiency-Cost Optimization (Fallani, Latora, & Chavez, 2017 ). Several network measures are translated from the Brain Connectivity Toolbox (Rubinov & Sporns, 2010 ). Brain methods include the recently developed Connectome Predictive Modeling (see references in package). Also implements several network measures including local network characteristics (e.g., centrality), community-level network characteristics (e.g., community centrality), global network characteristics (e.g., clustering coefficient), and various other measures associated with the reliability and reproducibility of 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) .

Data-Driven Causal Loop and Feedback Network Analysis

Provides tools for constructing signed causal-loop models, discovering directed causal relationships from time-series data using Granger-style tests, identifying and classifying reinforcing and balancing feedback loops, quantifying loop strength, assessing loop stability by bootstrap resampling, calculating network centrality and leverage-point scores, comparing causal-loop models, and producing publication-ready base R visualizations and summaries. The package is domain-agnostic and can be used in human medicine, veterinary medicine, agriculture, epidemiology, ecology, public health, and One Health. Methods are based on Granger (1969) and Efron (1979) .