Functions to calculate the assortment of vertices in social networks.  This can be measured on both weighted and binary networks, with discrete or continuous vertex values.

A 'shiny' application to assess statistical assumptions and guide users toward appropriate tests. The app is designed for researchers with minimal statistical training and provides diagnostics, plots, and test recommendations for a wide range of analyses. Many statistical assumptions are implemented using the package 'rstatix' (Kassambara, 2019) <doi:10.32614/CRAN.package.rstatix> and 'performance' (Lüdecke et al., 2021) <doi:10.21105/joss.03139>.

Fits a model to adjust and consider additional variations in three dimensions of age groups, time, and space on residuals excluded from a prediction model that have residual such as: linear regression, mixed model and so on. Details are given in Foreman et al. (2015) <doi:10.1186/1478-7954-10-1>.

Enable translation of a tiny subset of R to C++. The user has to define a R function which gets translated. For a full list of possible functions check the documentation. After translation an R function is returned which is a shallow wrapper around the C++ code. Alternatively an external pointer to the C++ function is returned to the user. The intention of the package is to generate fast functions which can be used as ode-system or during optimization.

Aster models (Geyer, Wagenius, and Shaw, 2007,
    <doi:10.1093/biomet/asm030>; Shaw, Geyer, Wagenius, Hangelbroek, and
    Etterson, 2008, <doi:10.1086/588063>; Geyer, Ridley, Latta, Etterson,
    and Shaw, 2013, <doi:10.1214/13-AOAS653>) are exponential family
    regression models for life
    history analysis.  They are like generalized linear models except that
    elements of the response vector can have different families (e. g.,
    some Bernoulli, some Poisson, some zero-truncated Poisson, some normal)
    and can be dependent, the dependence indicated by a graphical structure.
    Discrete time survival analysis, life table analysis,
    zero-inflated Poisson regression, and
    generalized linear models that are exponential family (e. g., logistic
    regression and Poisson regression with log link) are special cases.
    Main use is for data in which there is survival over discrete time periods
    and there is additional data about what happens conditional on survival
    (e. g., number of offspring).  Uses the exponential family canonical
    parameterization (aster transform of usual parameterization).
    There are also random effects versions of these models.