IMIFA (2.1.1)

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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) . 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.

Maintainer: Keefe Murphy
Author(s): Keefe Murphy [aut, cre], Cinzia Viroli [ctb], Isobel Claire Gormley [ctb]

License: GPL (>= 2)

Uses: matrixStats, mclust, mvnfast, Rfast, slam, viridis, gmp, mcclust, Rmpfr, knitr, rmarkdown

Released about 1 month ago.

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