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Model data with a suspected clustering structure (either in co-variate space, regression space or both) using a Bayesian product model with a logistic regression likelihood. Observations are represented graphically and clusters are formed through various edge removals or additions. Cluster quality is assessed through the log Bayesian evidence of the overall model, which is estimated using either a Sequential Monte Carlo sampler or a suitable transformation of the Bayesian Information Criterion as a fast approximation of the former. The internal Iterated Batch Importance Sampling scheme (Chopin (2002 <doi:10.1093/biomet/89.3.539>)) is made available as a free standing function.
| Version: | 1.1.0 |
| Imports: | mvnfast, igraph, crayon, memoise, GGally, ggplot2, ggpubr, scales, stats, cachem, ggnewscale |
| Published: | 2023-08-25 |
| Author: | Samuel Emerson [aut, cre] |
| Maintainer: | Samuel Emerson <samuel.emerson at hotmail.co.uk> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| Materials: | NEWS |
| CRAN checks: | UNCOVER results |
| Reference manual: | UNCOVER.pdf |
| Package source: | UNCOVER_1.1.0.tar.gz |
| Windows binaries: | r-devel: UNCOVER_1.1.0.zip, r-release: UNCOVER_1.1.0.zip, r-oldrel: UNCOVER_1.1.0.zip |
| macOS binaries: | r-release (arm64): UNCOVER_1.1.0.tgz, r-oldrel (arm64): UNCOVER_1.1.0.tgz, r-release (x86_64): UNCOVER_1.1.0.tgz, r-oldrel (x86_64): UNCOVER_1.1.0.tgz |
| Old sources: | UNCOVER archive |
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