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. (2020) <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: 2.2.0
Depends: R (≥ 4.0.0)
Imports: matrixStats (≥ 1.0.0), mclust (≥ 5.4), mvnfast, Rfast (≥ 1.9.8), slam, viridisLite
Suggests: gmp (≥ 0.5-4), knitr, mcclust, rmarkdown, Rmpfr
Published: 2023-12-12
DOI: 10.32614/CRAN.package.IMIFA
Author: Keefe Murphy ORCID iD [aut, cre], Cinzia Viroli ORCID iD [ctb], Isobel Claire Gormley ORCID iD [ctb]
Maintainer: Keefe Murphy <keefe.murphy at>
License: GPL (≥ 3)
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_2.2.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): IMIFA_2.2.0.tgz, r-oldrel (arm64): IMIFA_2.2.0.tgz, r-release (x86_64): IMIFA_2.2.0.tgz, r-oldrel (x86_64): IMIFA_2.2.0.tgz
Old sources: IMIFA archive


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