UNCOVER: Utilising Normalisation Constant Optimisation via Edge Removal (UNCOVER)

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
DOI: 10.32614/CRAN.package.UNCOVER
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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