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Stochastic collapsed variational inference on mixed-membership stochastic blockmodel for networks, incorporating node-level predictors of mixed-membership vectors, as well as dyad-level predictors. For networks observed over time, the model defines a hidden Markov process that allows the effects of node-level predictors to evolve in discrete, historical periods. In addition, the package offers a variety of utilities for exploring results of estimation, including tools for conducting posterior predictive checks of goodness-of-fit and several plotting functions. The package implements methods described in Olivella, Pratt and Imai (2019) 'Dynamic Stochastic Blockmodel Regression for Social Networks: Application to International Conflicts', available at <https://www.santiagoolivella.info/pdfs/socnet.pdf>.
Version: | 0.2.0.2 |
Depends: | R (≥ 4.1.0) |
Imports: | clue (≥ 0.3-58), graphics (≥ 3.5.2), grDevices (≥ 3.5.2), gtools (≥ 3.8.1), igraph (≥ 1.2.4.1), lda (≥ 1.4.2), Matrix (≥ 1.2-15), MASS (≥ 7.3-51.4), methods (≥ 3.5.2), poisbinom (≥ 1.0.1), Rcpp (≥ 1.0.2), stats (≥ 3.5.2), utils (≥ 3.5.2) |
LinkingTo: | Rcpp, RcppArmadillo |
Suggests: | ergm (≥ 3.9.4), ggplot2 (≥ 3.1.1), network (≥ 1.13), scales (≥ 1.0.0) |
Published: | 2023-12-13 |
DOI: | 10.32614/CRAN.package.NetMix |
Author: | Santiago Olivella [aut, cre], Adeline Lo [aut, cre], Tyler Pratt [aut, cre], Kosuke Imai [aut, cre] |
Maintainer: | Santiago Olivella <olivella at unc.edu> |
BugReports: | https://github.com/solivella/NetMix/issues |
License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
NeedsCompilation: | yes |
Materials: | README NEWS |
CRAN checks: | NetMix results |
Reference manual: | NetMix.pdf |
Package source: | NetMix_0.2.0.2.tar.gz |
Windows binaries: | r-devel: NetMix_0.2.0.2.zip, r-release: NetMix_0.2.0.2.zip, r-oldrel: NetMix_0.2.0.2.zip |
macOS binaries: | r-release (arm64): NetMix_0.2.0.2.tgz, r-oldrel (arm64): NetMix_0.2.0.2.tgz, r-release (x86_64): NetMix_0.2.0.2.tgz, r-oldrel (x86_64): NetMix_0.2.0.2.tgz |
Old sources: | NetMix archive |
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