eigenmodel

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.

Last 30 days

This package has been downloaded 1,824 times in the last 30 days. Now we’re talking! This work is officially 'heard of in academic circles', just like those wild research papers on synthetic bananas. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 73 times.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Last 365 days

This package has been downloaded 19,688 times in the last 365 days. The academic equivalent of having a dedicated subreddit. There are fans, and maybe even a few trolls! The day with the most downloads was Mar 15, 2025 with 172 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.