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 = 'https://cloud.r-project.org')
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

On CRAN:

Conda:

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

openblascpp

2.00 score 148 downloads 9 exports 4 dependencies

Last updated 4 years agofrom:106a3d6d30. Checks:3 OK. Indexed: no.

TargetResultLatest binary
Doc / VignettesOKApr 01 2025
R-4.5-linux-x86_64OKApr 01 2025
R-4.4-linux-x86_64OKApr 01 2025

Exports:computeBICcomputeELBOfindLabelsmixedMemModelmmVarFitpermuteLabelsrmixedMemvizMemvizTheta

Dependencies:BHgtoolsRcppRcppArmadillo

mixedMem

Rendered frommixedMem.Rnwusingknitr::knitron Apr 01 2025.

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

Citation

To cite package ‘mixedMem’ in publications use:

Wang Y, Erosheva E (2020). mixedMem: Tools for Discrete Multivariate Mixed Membership Models. R package version 1.1.2, https://CRAN.R-project.org/package=mixedMem.

Corresponding BibTeX entry: 

  @Manual{,
    title = {mixedMem: Tools for Discrete Multivariate Mixed Membership
      Models},
    author = {Y. Samuel Wang and Elena A. Erosheva},
    year = {2020},
    note = {R package version 1.1.2},
    url = {https://CRAN.R-project.org/package=mixedMem},
  }