ulrb: Unsupervised Learning Based Definition of Microbial Rare Biosphere

A tool to define rare biosphere. 'ulrb' solves the problem of the definition of rarity by replacing arbitrary thresholds with an unsupervised machine learning algorithm (partitioning around medoids, or k-medoids). This algorithm works for any type of microbiome data, provided there is a species abundance table. For validation of this method to different species abundance tables see Pascoal et al, 2024 (in peer-review). This method also works for non-microbiome data.

Version:	0.1.5
Depends:	R (≥ 2.10)
Imports:	cluster, dplyr, ggplot2, purrr, rlang, stats, tidyr, clusterSim, gridExtra
Suggests:	knitr, rmarkdown, stringr, testthat (≥ 3.0.0), vegan
Published:	2024-06-18
DOI:	10.32614/CRAN.package.ulrb
Author:	Francisco Pascoal [aut, cre], Paula Branco [aut], Luís Torgo [aut], Rodrigo Costa [aut], Catarina Magalhães [aut]
Maintainer:	Francisco Pascoal <fpascoal1996 at gmail.com>
BugReports:	https://github.com/pascoalf/ulrb/issues
License:	GPL (≥ 3)
URL:	https://pascoalf.github.io/ulrb/
NeedsCompilation:	no
Citation:	ulrb citation info
Materials:	README
CRAN checks:	ulrb results

Documentation:

Reference manual:	ulrb.pdf
Vignettes:	Glossary Integration of ulrb in a simple microbial ecology workflow Alternative classifications with ulrb Tutorial to define rare biosphere with ulrb

Downloads:

Package source:	ulrb_0.1.5.tar.gz
Windows binaries:	r-devel: ulrb_0.1.5.zip, r-release: ulrb_0.1.5.zip, r-oldrel: ulrb_0.1.5.zip
macOS binaries:	r-release (arm64): ulrb_0.1.5.tgz, r-oldrel (arm64): ulrb_0.1.5.tgz, r-release (x86_64): ulrb_0.1.5.tgz, r-oldrel (x86_64): ulrb_0.1.5.tgz
Old sources:	ulrb archive

Linking:

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