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) <arXiv:2009.07662>.

Version: 1.0.0
Depends: R (≥ 3.5)
Imports: Rcpp, parallel, sn, rmutil
LinkingTo: Rcpp, RcppArmadillo
Published: 2021-07-16
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

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Reference manual: MNARclust.pdf
Package source: MNARclust_1.0.0.tar.gz
Windows binaries: r-devel: MNARclust_1.0.0.zip, r-devel-UCRT: MNARclust_1.0.0.zip, r-release: MNARclust_1.0.0.zip, r-oldrel: MNARclust_1.0.0.zip
macOS binaries: r-release (arm64): MNARclust_1.0.0.tgz, r-release (x86_64): MNARclust_1.0.0.tgz, r-oldrel: MNARclust_1.0.0.tgz

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