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 
    article Wijnhorst et al. (2026) <doi:10.1093/jeb/voag013>.

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.

Provides an interface to search, read, query, and retrieve metadata for 
    datasets hosted on 'Socrata' open data portals. Supports all 'Socrata' data types, 
    including spatial data returned as 'sf' objects.

Supporting materials for a course and book on data visualization. It contains utility functions for graphs and several sample data sets. See Healy (2019) <ISBN 978-0691181622>.

Variable and interaction selection are essential to classification in high-dimensional setting. In this package, we provide the implementation of SODA procedure, which is a forward-backward algorithm that selects both main and interaction effects under logistic regression and quadratic discriminant analysis. We also provide an extension, S-SODA, for dealing with the variable selection problem for semi-parametric models with continuous responses.