Package: eigenmodel 1.11
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) <arxiv:0711.1146> for details on the model.
Authors:
eigenmodel_1.11.tar.gz
eigenmodel_1.11.zip(r-4.5)eigenmodel_1.11.zip(r-4.4)eigenmodel_1.11.zip(r-4.3)
eigenmodel_1.11.tgz(r-4.4-any)eigenmodel_1.11.tgz(r-4.3-any)
eigenmodel_1.11.tar.gz(r-4.5-noble)eigenmodel_1.11.tar.gz(r-4.4-noble)
eigenmodel_1.11.tgz(r-4.4-emscripten)eigenmodel_1.11.tgz(r-4.3-emscripten)
eigenmodel.pdf |eigenmodel.html✨
eigenmodel/json (API)
| # Install 'eigenmodel' in R: |
| install.packages('eigenmodel', repos = c('https://pdhoff.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 5 years agofrom:d4c9f03f0b. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 31 2024 |
| R-4.5-win | OK | Oct 31 2024 |
| R-4.5-linux | OK | Oct 31 2024 |
| R-4.4-win | OK | Oct 31 2024 |
| R-4.4-mac | OK | Oct 31 2024 |
| R-4.3-win | OK | Oct 31 2024 |
| R-4.3-mac | OK | Oct 31 2024 |
Exports:addlineseigenmodel_mcmceigenmodel_setupplot.eigenmodel_postrb_fcrmvnormrUL_fcrZ_fcULUXBY_impute
Dependencies:
