Numero: Statistical Framework to Define Subgroups in Complex Datasets

High-dimensional datasets that do not exhibit a clear intrinsic clustered structure pose a challenge to conventional clustering algorithms. For this reason, we developed an unsupervised framework that helps scientists to better subgroup their datasets based on visual cues, please see Gao S, Mutter S, Casey A, Makinen V-P (2019) Numero: a statistical framework to define multivariable subgroups in complex population-based datasets, Int J Epidemiology, 48:369-37, <doi:10.1093/ije/dyy113>. The framework includes the necessary functions to construct a self-organizing map of the data, to evaluate the statistical significance of the observed data patterns, and to visualize the results.

Version:	1.9.8
Imports:	Rcpp (≥ 1.0.0)
LinkingTo:	Rcpp
Suggests:	knitr, rmarkdown
Published:	2024-09-17
DOI:	10.32614/CRAN.package.Numero
Author:	Song Gao [aut], Stefan Mutter [aut], Aaron E. Casey [aut], Ville-Petteri Makinen [aut, cre]
Maintainer:	Ville-Petteri Makinen <vpmakine at gmail.com>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	yes
Citation:	Numero citation info
CRAN checks:	Numero results

Documentation:

Reference manual:	Numero.pdf
Vignettes:	intro (source, R code)

Downloads:

Package source:	Numero_1.9.8.tar.gz
Windows binaries:	r-devel: Numero_1.9.8.zip, r-release: Numero_1.9.8.zip, r-oldrel: Numero_1.9.8.zip
macOS binaries:	r-release (arm64): Numero_1.9.8.tgz, r-oldrel (arm64): Numero_1.9.8.tgz, r-release (x86_64): Numero_1.9.8.tgz, r-oldrel (x86_64): Numero_1.9.8.tgz
Old sources:	Numero archive

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