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. (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: |
2.1.9 |
| Depends: |
R (≥ 4.0.0) |
| Imports: |
matrixStats (≥ 0.53.1), mclust (≥ 5.4), mvnfast, Rfast (≥
1.9.8), slam, viridisLite |
| Suggests: |
gmp (≥ 0.5-4), knitr, mcclust, rmarkdown, Rmpfr |
| Published: |
2022-08-15 |
| 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-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 |
Documentation:
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