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. (2018) <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, posterior predictive checking, and quantifying uncertainty.
|Author||Keefe Murphy [aut, cre], Cinzia Viroli [ctb], Isobel Claire Gormley [ctb]|
|Maintainer||Keefe Murphy <[email protected]>|
|License||GPL (>= 2)|
|Package repository||View on CRAN|
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