Semiparametric Factor and Regression Models for Symmetric Relational Data

Estimation of the parameters in 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 accommodated, 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. See Hoff (2007) for details on the model.


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install.packages("eigenmodel")

1.11 by Peter Hoff, 3 years ago


https://pdhoff.github.io/


Browse source code at https://github.com/cran/eigenmodel


Authors: Peter Hoff


Documentation:   PDF Manual  


Task views: Bayesian Inference, Missing Data


GPL-2 license



Imported by networktools.

Suggested by sand.


See at CRAN