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>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.1.0 |
rolling linux/jammy R-4.5 | MNARclust_1.1.0.tar.gz |
85.0 KiB |
1.1.0 |
rolling linux/noble R-4.5 | MNARclust_1.1.0.tar.gz |
86.0 KiB |
1.1.0 |
rolling source/ R- | MNARclust_1.1.0.tar.gz |
12.0 KiB |
1.1.0 |
latest linux/jammy R-4.5 | MNARclust_1.1.0.tar.gz |
85.0 KiB |
1.1.0 |
latest linux/noble R-4.5 | MNARclust_1.1.0.tar.gz |
86.0 KiB |
1.1.0 |
latest source/ R- | MNARclust_1.1.0.tar.gz |
12.0 KiB |
1.1.0 |
2026-04-26 source/ R- | MNARclust_1.1.0.tar.gz |
12.0 KiB |
1.1.0 |
2026-04-23 source/ R- | MNARclust_1.1.0.tar.gz |
12.0 KiB |
1.1.0 |
2026-04-09 windows/windows R-4.5 | MNARclust_1.1.0.zip |
408.2 KiB |
1.1.0 |
2025-04-20 source/ R- | MNARclust_1.1.0.tar.gz |
12.0 KiB |