mixedMem: Tools for Discrete Multivariate Mixed Membership Models

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
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

Documentation:

Reference manual:	mixedMem.pdf
Vignettes:	mixedMem

Downloads:

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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