eigenmodel: 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) <doi:10.48550/arXiv.0711.1146> for details on the model.

Version: 1.11
Published: 2019-05-28
DOI: 10.32614/CRAN.package.eigenmodel
Author: Peter Hoff
Maintainer: Peter Hoff <peter.hoff at duke.edu>
License: GPL-2
URL: https://pdhoff.github.io/
NeedsCompilation: no
Materials: NEWS
In views: Bayesian, MissingData
CRAN checks: eigenmodel results


Reference manual: eigenmodel.pdf


Package source: eigenmodel_1.11.tar.gz
Windows binaries: r-devel: eigenmodel_1.11.zip, r-release: eigenmodel_1.11.zip, r-oldrel: eigenmodel_1.11.zip
macOS binaries: r-release (arm64): eigenmodel_1.11.tgz, r-oldrel (arm64): eigenmodel_1.11.tgz, r-release (x86_64): eigenmodel_1.11.tgz, r-oldrel (x86_64): eigenmodel_1.11.tgz
Old sources: eigenmodel archive

Reverse dependencies:

Reverse imports: networktools
Reverse suggests: sand


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