Description: 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. (2017) <https://arxiv.org/abs/1701.07010>. 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 and conducting posterior inference on parameters of interest.
Imports: abind, corpcor, e1071, gclus, graphics, grDevices, MASS,
matrixcalc, matrixStats, mclust, MCMCpack, mvnfast, plotrix,
Suggests: Rmpfr, gmp, knitr, methods, rmarkdown

Description: 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. (2017) <arXiv:1701.07010>. 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 and conducting posterior inference on parameters of interest.
Imports: abind, e1071, graphics, grDevices, matrixStats, mclust,
mvnfast, plotrix, Rfast, slam, stats, utils, viridis

Title: Infinite Mixtures of Infinite Factor Analysers and Related
person("Isobel Claire", "Gormley", email = "claire.gormley@ucd.ie", role = "ctb"),
person("Cinzia", "Viroli", email = "cinzia.viroli@unibo.it", role = "ctb"))
Description: 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. (2017) <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, and quantifying uncertainty.
Imports: matrixStats, mclust (>= 5.1), mvnfast, Rfast (>= 1.8.4), slam,
Suggests: gmp, knitr, mcclust, methods, rmarkdown, Rmpfr