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| Package: IMIFA | ||
| Type: Package | ||
| Date: 2020-05-12 | ||
| Date: 2020-11-18 | ||
| Title: Infinite Mixtures of Infinite Factor Analysers and Related | ||
| Models | ||
| Version: 2.1.3 | ||
| Authors@R: c(person("Keefe", "Murphy", email = "keefe.murphy@ucd.ie", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-7709-3159")), | ||
| Version: 2.1.4 | ||
| Authors@R: c(person("Keefe", "Murphy", email = "keefe.murphy@mu.ie", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-7709-3159")), | ||
| person("Cinzia", "Viroli", email = "cinzia.viroli@unibo.it", role = "ctb"), | ||
| person("Isobel Claire", "Gormley", email = "claire.gormley@ucd.ie", 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. (2019) <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. | ||
| 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. (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. | ||
| Depends: R (>= 4.0.0) | ||
| License: GPL (>= 2) | ||
| Encoding: UTF-8 | ||
| URL: https://cran.r-project.org/package=IMIFA | ||
| BugReports: https://github.com/Keefe-Murphy/IMIFA | ||
| LazyData: true | ||
| Imports: matrixStats, mclust (>= 5.1), mvnfast, Rfast (>= 1.9.8), slam, | ||
| viridis | ||
| Suggests: gmp, knitr, mcclust, rmarkdown, Rmpfr | ||
| RoxygenNote: 7.1.0 | ||
| Imports: matrixStats (>= 0.53.1), mclust (>= 5.4), mvnfast, Rfast (>= | ||
| 1.9.8), slam, viridisLite | ||
| Suggests: gmp (>= 0.5-4), knitr, mcclust, rmarkdown, Rmpfr | ||
| RoxygenNote: 7.1.1 | ||
| VignetteBuilder: knitr | ||
| Collate: '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' | ||
| NeedsCompilation: no | ||
| Packaged: 2020-05-12 15:23:52 UTC; Keefe | ||
| Packaged: 2020-11-18 13:18:19 UTC; Keefe | ||
| Author: Keefe Murphy [aut, cre] (<https://orcid.org/0000-0002-7709-3159>), | ||
| Cinzia Viroli [ctb], | ||
| Isobel Claire Gormley [ctb] | ||
| Maintainer: Keefe Murphy <keefe.murphy@ucd.ie> | ||
| Maintainer: Keefe Murphy <keefe.murphy@mu.ie> | ||
| Repository: CRAN | ||
| Date/Publication: 2020-05-12 16:10:02 UTC | ||
| Date/Publication: 2020-11-18 23:10:16 UTC |
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