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:	2.1.3
Depends:	R (≥ 4.0.0)
Imports:	matrixStats, mclust (≥ 5.1), mvnfast, Rfast (≥ 1.9.8), slam, viridis
Suggests:	gmp, knitr, mcclust, rmarkdown, Rmpfr
Published:	2020-05-12
Author:	Keefe Murphy [aut, cre], Cinzia Viroli [ctb], Isobel Claire Gormley [ctb]
Maintainer:	Keefe Murphy <keefe.murphy at ucd.ie>
BugReports:	https://github.com/Keefe-Murphy/IMIFA
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL:	https://cran.r-project.org/package=IMIFA
NeedsCompilation:	no
Citation:	IMIFA citation info
Materials:	README NEWS
In views:	Cluster
CRAN checks:	IMIFA results

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