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

2.15 score 14 scripts 174 downloads 9 exports 4 dependencies

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

TargetResultDate
Doc / VignettesOKDec 02 2024
R-4.5-linux-x86_64OKDec 02 2024

Exports:computeBICcomputeELBOfindLabelsmixedMemModelmmVarFitpermuteLabelsrmixedMemvizMemvizTheta

Dependencies:BHgtoolsRcppRcppArmadillo

mixedMem

Rendered frommixedMem.Rnwusingknitr::knitron Dec 02 2024.

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