Estimate influence networks from longitudinal bipartite relational data, where the longitudinal relations are continuous. The outputs are estimates of weighted influence networks among each actor type in the data set. The generative model is the Bipartite Longitudinal Influence Network (BLIN) model, a linear autoregressive model for these type of data. The supporting paper is “Inferring Influence Networks from Longitudinal Bipartite Relational Data”, which is in preparation by the same authors. The model may be estimated using maximum likelihood methods and Bayesian methods. For more detail on methods, see Marrs et. al. <arXiv:1809.03439>.
| Version: | 0.0.1 |
| Depends: | R (≥ 3.3.0) |
| Imports: | glmnet, stats, Matrix, MASS, abind, graphics, mvtnorm |
| Suggests: | knitr, knitcitations |
| Published: | 2018-09-21 |
| Author: | Frank W. Marrs [aut, cre], Benjamin W. Campbell [aut], Bailey K. Fosdick [aut], Skyler J. Cranmer [aut], Tobias B{"o}hmelt [aut] |
| Maintainer: | Frank W. Marrs <frank.marrs at colostate.edu> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | no |
| CRAN checks: | blin results |
| Reference manual: | blin.pdf |
| Vignettes: |
Regression with Network Response |
| Package source: | blin_0.0.1.tar.gz |
| Windows binaries: | r-devel: blin_0.0.1.zip, r-release: blin_0.0.1.zip, r-oldrel: blin_0.0.1.zip |
| macOS binaries: | r-release: blin_0.0.1.tgz, r-oldrel: blin_0.0.1.tgz |
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