Dyadic data analysis with amen

TL;DR

The paper introduces the 'amen' R package for modeling complex dependencies in dyadic data using additive and multiplicative effects, accommodating various data types and missingness.

Contribution

It presents the 'amen' package, enabling flexible estimation of dyadic data models with diverse dependency structures and data types.

Findings

Effective modeling of dyadic dependencies

Supports multiple data types and missing data

Provides comprehensive tools for dyadic data analysis

Abstract

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.