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. 
    The package interacts with Stan via 'cmdstanr' (available from 
    <https://mc-stan.org/r-packages/>) or 'rstan', depending on user setup. 
    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 more 
    details on the study of social interactions and impact-responsiveness,
    see Moore et al. (1997) <doi:10.1111/j.1558-5646.1997.tb01458.x> and 
    de Groot et al. (2022) <doi:10.1016/j.neubiorev.2022.104996>.

Accesses raw data via API and calculates social
    determinants of health measures for user-specified locations in the
    US, returning them in tidyverse- and sf-compatible data frames.