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Fits mixed membership models with discrete multivariate data (with or without repeated measures) following the general framework of Erosheva et al (2004). This package uses a Variational EM approach by approximating the posterior distribution of latent memberships and selecting hyperparameters through a pseudo-MLE procedure. Currently supported data types are Bernoulli, multinomial and rank (Plackett-Luce). The extended GoM model with fixed stayers from Erosheva et al (2007) is now also supported. See Airoldi et al (2014) for other examples of mixed membership models.
| Version: | 1.1.2 |
| Depends: | R (≥ 3.0.2) |
| Imports: | Rcpp (≥ 0.11.3), gtools |
| LinkingTo: | Rcpp (≥ 0.11.3), RcppArmadillo, BH |
| Suggests: | knitr, xtable |
| Published: | 2020-12-01 |
| DOI: | 10.32614/CRAN.package.mixedMem |
| Author: | Y. Samuel Wang [aut, cre], Elena A. Erosheva [aut] |
| Maintainer: | Y. Samuel Wang <ysamuelwang at gmail.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | yes |
| CRAN checks: | mixedMem results |
| Reference manual: | mixedMem.pdf |
| Vignettes: |
mixedMem |
| Package source: | mixedMem_1.1.2.tar.gz |
| Windows binaries: | r-devel: mixedMem_1.1.2.zip, r-release: mixedMem_1.1.2.zip, r-oldrel: mixedMem_1.1.2.zip |
| macOS binaries: | r-release (arm64): mixedMem_1.1.2.tgz, r-oldrel (arm64): mixedMem_1.1.2.tgz, r-release (x86_64): mixedMem_1.1.2.tgz, r-oldrel (x86_64): mixedMem_1.1.2.tgz |
| Old sources: | mixedMem archive |
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