Package: MNARclust 1.1.0

Matthieu Marbac

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>.

Authors:Marie Du Roy de Chaumaray [aut], Matthieu Marbac [aut, cre, cph]

MNARclust_1.1.0.tar.gz
MNARclust_1.1.0.tar.gz(r-4.5-noble)MNARclust_1.1.0.tar.gz(r-4.4-noble)
MNARclust_1.1.0.tgz(r-4.4-emscripten)MNARclust_1.1.0.tgz(r-4.3-emscripten)
MNARclust.pdf |MNARclust.html
MNARclust/json (API)

# Install 'MNARclust' in R:
install.packages('MNARclust', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:
  • echo - Echocardiogram data set

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

openblascpp

1.00 score 151 downloads 2 exports 13 dependencies

Last updated 3 years agofrom:f8ad70770a. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKDec 08 2024
R-4.5-linux-x86_64OKDec 08 2024

Exports:MNARclusterrMNAR

Dependencies:latticeMASSMatrixMatrixModelsmnormtnumDerivquantregRcppRcppArmadillormutilsnSparseMsurvival