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MNARclust: Clustering Data with Non-Ignorable Missingness usingSemi-Parametric Mixture Models

Clustering of data under a non-ignorable missingness mechanism. Clustering is achieved by a semi-parametric mixture model and missingness is managed by using the pattern-mixture approach. More details of the approach are available in Du Roy de Chaumaray et al. (2020) <doi:10.48550/arXiv.2009.07662>.

Version:1.1.0
Depends:R (≥ 3.5)
Imports:Rcpp, parallel,sn,rmutil
LinkingTo:Rcpp,RcppArmadillo
Published:2021-12-02
DOI:10.32614/CRAN.package.MNARclust
Author:Marie Du Roy de Chaumaray [aut], Matthieu Marbac [aut, cre, cph]
Maintainer:Matthieu Marbac <matthieu.marbac-lourdelle at ensai.fr>
License:GPL-2 |GPL-3 [expanded from: GPL (≥ 2)]
URL:https://arxiv.org/abs/2009.07662
NeedsCompilation:yes
CRAN checks:MNARclust results

Documentation:

Reference manual:MNARclust.html ,MNARclust.pdf

Downloads:

Package source: MNARclust_1.1.0.tar.gz
Windows binaries: r-devel:MNARclust_1.1.0.zip, r-release:MNARclust_1.1.0.zip, r-oldrel:MNARclust_1.1.0.zip
macOS binaries: r-release (arm64):MNARclust_1.1.0.tgz, r-oldrel (arm64):MNARclust_1.1.0.tgz, r-release (x86_64):MNARclust_1.1.0.tgz, r-oldrel (x86_64):MNARclust_1.1.0.tgz
Old sources: MNARclust archive

Linking:

Please use the canonical formhttps://CRAN.R-project.org/package=MNARclustto link to this page.


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