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UNCOVER  

Utilising Normalisation Constant Optimisation via Edge Removal (UNCOVER)
View on CRAN: Click here


Download and install UNCOVER package within the R console
Install from CRAN:
install.packages("UNCOVER")

Install from Github:
library("remotes")
install_github("cran/UNCOVER")

Install by package version:
library("remotes")
install_version("UNCOVER", "1.1.0")



Attach the package and use:
library("UNCOVER")
Maintained by
Samuel Emerson
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2023-02-20
Latest Update: 2023-08-25
Description:
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.
How to cite:
Samuel Emerson (2023). UNCOVER: Utilising Normalisation Constant Optimisation via Edge Removal (UNCOVER). R package version 1.1.0, https://cran.r-project.org/web/packages/UNCOVER. Accessed 22 Dec. 2024.
Previous versions and publish date:
1.0.0 (2023-02-20 12:20)
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Complete documentation for UNCOVER
Functions, R codes and Examples using the UNCOVER R package
Full UNCOVER package functions and examples
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