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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 |
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 |
Reference manual: | IMIFA.pdf |
Vignettes: |
Infinite Mixtures of Infinite Factor Analysers |
Package source: | IMIFA_2.2.0.tar.gz |
Windows binaries: | r-devel: IMIFA_2.2.0.zip, r-release: IMIFA_2.2.0.zip, r-oldrel: IMIFA_2.2.0.zip |
macOS binaries: | r-release (arm64): IMIFA_2.2.0.tgz, r-oldrel (arm64): IMIFA_2.2.0.tgz, r-release (x86_64): IMIFA_2.2.0.tgz, r-oldrel (x86_64): IMIFA_2.2.0.tgz |
Old sources: | IMIFA archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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