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. (2019) <doi:10.1214/19-BA1179>. 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, posterior predictive checking, and quantifying uncertainty.

Version: 2.1.2
Depends: R (≥ 3.3.0)
Imports: matrixStats, mclust (≥ 5.1), mvnfast, Rfast (≥ 1.9.8), slam, viridis
Suggests: gmp, knitr, mcclust, rmarkdown, Rmpfr
Published: 2020-03-30
Author: Keefe Murphy ORCID iD [aut, cre], Cinzia Viroli [ctb], Isobel Claire Gormley [ctb]
Maintainer: Keefe Murphy <keefe.murphy at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Citation: IMIFA citation info
Materials: README NEWS
In views: Cluster
CRAN checks: IMIFA results


Reference manual: IMIFA.pdf
Vignettes: Infinite Mixtures of Infinite Factor Analysers
Package source: IMIFA_2.1.2.tar.gz
Windows binaries: r-prerelease:, r-release:, r-oldrel:
macOS binaries: r-prerelease: IMIFA_2.1.2.tgz, r-release: IMIFA_2.1.2.tgz, r-oldrel: IMIFA_2.1.1.tgz
Old sources: IMIFA archive


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