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>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
CRAN checks: MNARclust results


Reference manual: MNARclust.pdf


Package source: MNARclust_1.1.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
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


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