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 [aut, cre], Cinzia Viroli [ctb], Isobel Claire Gormley [ctb] Maintainer: Keefe Murphy <keefe.murphy at mu.ie> BugReports: https://github.com/Keefe-Murphy/IMIFA License: GPL (≥ 3) URL: https://cran.r-project.org/package=IMIFA NeedsCompilation: no Citation: IMIFA citation info Materials: README NEWS In views: Cluster CRAN checks: IMIFA results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=IMIFA to link to this page.
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