  Dyadic data on pairs of objects, such as relational or social network data,
often exhibit strong statistical dependencies. Certain types of second-order
dependencies, such as degree heterogeneity and reciprocity, can be
well-represented with additive random effects models. Higher-order
dependencies, such as transitivity and stochastic equivalence, can often be
represented with multiplicative effects. The "amen" package for the R
statistical computing environment provides estimation and inference for a class
of additive and multiplicative random effects models for ordinal, continuous,
binary and other types of dyadic data. The package also provides methods for
missing, censored and fixed-rank nomination data, as well as longitudinal
dyadic data. This tutorial illustrates the "amen" package via example
statistical analyses of several of these different data types.
