IMIFA: Infinite Mixtures of Infinite Factor Analysers and RelatedModels
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. (2020) <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: | 2.2.0 |
| Depends: | R (≥ 4.0.0) |
| Imports: | matrixStats (≥ 1.0.0),mclust (≥ 5.4),mvnfast,Rfast (≥1.9.8),slam,viridisLite |
| Suggests: | gmp (≥ 0.5-4),knitr,mcclust,rmarkdown,Rmpfr |
| Published: | 2023-12-12 |
| DOI: | 10.32614/CRAN.package.IMIFA |
| Author: | Keefe Murphy [aut, cre], Cinzia Viroli [ctb], Isobel Claire Gormley [ctb] |
| Maintainer: | Keefe Murphy <keefe.murphy at mu.ie> |
| BugReports: | https://github.com/Keefe-Murphy/IMIFA |
| License: | GPL (≥ 3) |
| 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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