IMIFA: Infinite Mixtures of Infinite Factor Analysers and Related Models

Provides flexible Bayesian estimation of Infinite Mixtures of Infinite Factor Analysers and related models, for nonparametrically clustering high-dimensional data, introduced by Murphy et al. (2017) <arXiv:1701.07010v4>. The IMIFA model conducts Bayesian nonparametric model-based clustering with factor analytic covariance structures without recourse to model selection criteria to choose the number of clusters or cluster-specific latent factors, mostly via efficient Gibbs updates. Model-specific diagnostic tools are also provided, as well as many options for plotting results, conducting posterior inference on parameters of interest, and quantifying uncertainty.

Version: 2.0.0
Depends: R (≥ 3.3.0)
Imports: matrixStats, mclust (≥ 5.1), mvnfast, Rfast (≥ 1.8.4), slam, viridis
Suggests: gmp, knitr, mcclust, methods, rmarkdown, Rmpfr
Published: 2018-05-01
Author: Keefe Murphy [aut, cre], Isobel Claire Gormley [ctb], Cinzia Viroli [ctb]
Maintainer: Keefe Murphy <keefe.murphy at ucd.ie>
BugReports: https://github.com/Keefe-Murphy/IMIFA
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://cran.r-project.org/package=IMIFA
NeedsCompilation: no
Citation: IMIFA citation info
Materials: README NEWS
In views: Cluster
CRAN checks: IMIFA results

Downloads:

Reference manual: IMIFA.pdf
Vignettes: Infinite Mixtures of Infinite Factor Analysers
Package source: IMIFA_2.0.0.tar.gz
Windows binaries: r-devel: IMIFA_2.0.0.zip, r-release: IMIFA_2.0.0.zip, r-oldrel: IMIFA_2.0.0.zip
OS X binaries: r-release: IMIFA_2.0.0.tgz, r-oldrel: IMIFA_2.0.0.tgz
Old sources: IMIFA archive

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