Package: mixedMem 1.1.2

Y. Samuel Wang

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.

Authors:Y. Samuel Wang [aut, cre], Elena A. Erosheva [aut]

mixedMem_1.1.2.tar.gz
mixedMem_1.1.2.tar.gz(r-4.5-noble)mixedMem_1.1.2.tar.gz(r-4.4-noble)
mixedMem_1.1.2.tgz(r-4.4-emscripten)mixedMem_1.1.2.tgz(r-4.3-emscripten)
mixedMem.pdf |mixedMem.html
mixedMem/json (API)

# Install 'mixedMem' in R:
install.packages('mixedMem', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:
  • ANES - Responses from 1983 American National Election Survey Pilot
  • gmv_theta - Point estimates from Gross and Manrique-Vallier 2014

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

openblascpp

2.00 score 230 downloads 9 exports 4 dependencies

Last updated 4 years agofrom:106a3d6d30. Checks:2 OK. Indexed: no.

TargetResultLatest binary
Doc / VignettesOKJan 31 2025
R-4.5-linux-x86_64OKJan 31 2025

Exports:computeBICcomputeELBOfindLabelsmixedMemModelmmVarFitpermuteLabelsrmixedMemvizMemvizTheta

Dependencies:BHgtoolsRcppRcppArmadillo

mixedMem

Rendered frommixedMem.Rnwusingknitr::knitron Jan 31 2025.

Last update: 2020-12-01
Started: 2015-04-29