The notion of power index has been widely used in literature to evaluate the influence of individual players (e.g., voters, political parties, nations, stockholders, etc.) involved in a collective decision situation like an electoral system, a parliament, a council, a management board, etc., where players may form coalitions. Traditionally this ranking is determined through numerical evaluation. More often than not however only ordinal data between coalitions is known. The package 'socialranking' offers a set of solutions to rank players based on a transitive ranking between coalitions, including through CP-Majority, ordinal Banzhaf or lexicographic excellence solution summarized by Tahar Allouche, Bruno Escoffier, Stefano Moretti and Meltem Öztürk (2020, <doi:10.24963/ijcai.2020/3>).

Social risks are increasingly becoming a critical component of health
    care research. One of the most common ways to identify social needs is by using
    ICD-10-CM "Z-codes." This package identifies social risks using varying taxonomies
    of ICD-10-CM Z-codes from administrative health care data. The conceptual
    taxonomies come from:
    Centers for Medicare and Medicaid Services (2021) <https://www.cms.gov/files/document/zcodes-infographic.pdf>,
    Reidhead (2018) <https://web.mhanet.com/>,
    A Arons, S DeSilvey, C Fichtenberg, L Gottlieb (2018) <https://sirenetwork.ucsf.edu/tools-resources/resources/compendium-medical-terminology-codes-social-risk-factors>.

Provides tools to simulate and analyse datasets of social interactions 
    between individuals using hierarchical Bayesian models implemented in Stan. 
    Model fitting is performed via the 'rstan' package. Users can generate 
    realistic interaction data where individual phenotypes influence and respond 
    to those of their partners, with control over sampling design parameters such 
    as the number of individuals, partners, and repeated dyads. The simulation 
    framework allows flexible control over variation and correlation in mean trait 
    values, social responsiveness, and social impact, making it suitable for 
    research on interacting phenotypes and on direct and indirect genetic effects 
    ('DGEs' and 'IGEs'). The package also includes functions to fit and compare 
    alternative models of social effects, including impact–responsiveness, 
    variance–partitioning, and trait-based models, and to summarise model 
    performance in terms of bias and dispersion. For a more detailed description 
    of the available models and impact–responsiveness, see the accompanying 
    preprint Wijnhorst et al. (2025) <doi:10.32942/X2F65M>.