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MNARclust: Clustering Data with Non-Ignorable Missingness using Semi-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.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 form https://CRAN.R-project.org/package=MNARclust to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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