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 |
Documentation:
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