UNCOVER
1.1.0Utilising Normalisation Constant Optimisation via Edge Removal (UNCOVER)
Overview
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.
Install
Health
- OK2026-06-0913 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-06-0812 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 25%
- Documented parameters
- 91%
- Return-value docs
- 100%
- References docs
- 40%
Downloads
Dependencies
Nothing depends on this yet.
Code & Tests
People & History
2 releases. Pick two to compare their code metrics. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- 1.1.0Latest
- RR 4.3.0 released · 2023-04-21
- 1.0.02023-02-20
- RR 4.2.0 released · 2022-04-22
Package metadata
- First published
- 2023-02-20
- Total releases
- 2 / 3 yrs
- License
- GPL-2 OSI
- Download size
- 40 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet