Skip to Content


Semiparametric factor and regression models for symmetric relational data
Peter Hoff
This package estimates the parameters of a model for symmetric relational data (e.g., the above-diagonal part of a square matrix), using a model-based eigenvalue decomposition and regression. Missing data is accomodated, and a posterior mean for missing data is calculated under the assumption that the data are missing at random. The marginal distribution of the relational data can be arbitrary, and is fit with an ordered probit specification.
Package Version Released
eigenmodel 1.01 3 years 36 weeks ago
eigenmodel 1.0
Your rating: None
Your rating: None