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.
abind, corpcor, e1071, gclus, graphics, grDevices, MASS,
matrixcalc, matrixStats, mclust, MCMCpack, mvnfast, plotrix,
Rfast, slam, stats, utils, viridis

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.
abind, e1071, graphics, grDevices, matrixStats, mclust,
mvnfast, plotrix, Rfast, slam, stats, utils, viridis

Infinite Mixtures of Infinite Factor Analysers and Related
Models
c(person("Keefe", "Murphy", email = "keefe.murphy@ucd.ie", role = c("aut", "cre")),
person("Isobel Claire", "Gormley", email = "claire.gormley@ucd.ie", role = "ctb"),
person("Cinzia", "Viroli", email = "cinzia.viroli@unibo.it", role = "ctb"))
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.
matrixStats, mclust (>= 5.1), mvnfast, Rfast (>= 1.8.4), slam,
viridis
gmp, knitr, mcclust, methods, rmarkdown, Rmpfr
'MainFunction.R' 'Diagnostics.R' 'FullConditionals.R'
'Gibbs_FA.R' 'Gibbs_IFA.R' 'Gibbs_IMFA.R' 'Gibbs_IMIFA.R'
'Gibbs_MFA.R' 'Gibbs_MIFA.R' 'Gibbs_OMFA.R' 'Gibbs_OMIFA.R'
'IMIFA.R' 'PlottingFunctions.R' 'SimulateData.R' 'data.R'