eigenmodel

Eigenmodel is a handy tool for looking at special types of data. It works great with data that is symmetric, like the top part of a square matrix. It uses smart methods like model-based eigenvalue decomposition and regression to figure things out accurately.

Benefits

Eigenmodel is awesome at handling missing data. It figures out the likely values for missing data, assuming the missing bits are random. This keeps your results strong and trustworthy, even with some data missing. Also, the way it looks at relational data is flexible, and it fits well with an ordered probit setup.

Use Cases

Eigenmodel is super useful for researchers and data analysts dealing with complex relational data. It can be used in many areas, like social network analysis and gene expression studies. Any place where symmetric relational data is common, Eigenmodel can help. Its ability to handle missing data makes it very useful for real-world situations where getting all the data can be tough.

Additional Information

The Eigenmodel package is created by Peter Hoff and is available under GPL-2. The latest version is 1.11, released on May 28, 2019. To get the package in R, use the command: install.packages("eigenmodel"). For more info, visit the package page on CRAN or the author''s GitHub page at https://pdhoff.github.io/.

To learn more about the model, check out Hoff (2007) .