Implements some risk measures for (financial) networks, such as DebtRank, Impact Susceptibility, Impact Diffusion and Impact Fluidity.

Provides a variety of Network Scale-up Models for researchers to 
    analyze Aggregated Relational Data, through the use of Stan and 'glmmTMB'.
    Also provides tools for model checking
    In this version, the package implements models from 
    Laga, I., Bao, L., and Niu, X (2023) <doi:10.1080/01621459.2023.2165929>,
    Zheng, T., Salganik, M. J., and Gelman, A. (2006) <doi:10.1198/016214505000001168>,
    Killworth, P. D., Johnsen, E. C., McCarty, C., Shelley, G. A., 
    and Bernard, H. R. (1998) <doi:10.1016/S0378-8733(96)00305-X>, and 
    Killworth, P. D., McCarty, C., Bernard, H. R., Shelley, G. A., and 
    Johnsen, E. C. (1998) <doi:10.1177/0193841X9802200205>.

Several methods have been developed to integrate structural equation modeling techniques with network data analysis to examine the relationship between network and non-network data. Both node-based and edge-based information can be extracted from the network data to be used as observed variables in structural equation modeling. To facilitate the application of these methods, model specification can be performed in the familiar syntax of the 'lavaan' package, ensuring ease of use for researchers. Technical details and examples can be found at <https://bigsem.psychstat.org>.

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 <doi:10.1371/journal.pone.0015032>), Information Filtering Networks (Barfuss, Massara, Di Matteo, & Aste, 2016 <doi:10.1103/PhysRevE.94.062306>), and Efficiency-Cost Optimization (Fallani, Latora, & Chavez, 2017 <doi:10.1371/journal.pcbi.1005305>). 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.