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.
- Version1.1.0
- R versionunknown
- LicenseGPL-2
- LicenseGPL-3
- Needs compilation?Yes
- Last release12/02/2021
Team
Matthieu Marbac
Marie Du Roy de Chaumaray
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Insights
Last 30 days
This package has been downloaded 152 times in the last 30 days. Now we're getting somewhere! Enough downloads to populate a lively group chat. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 7 times.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Last 365 days
This package has been downloaded 2,259 times in the last 365 days. That's enough downloads to impress a room full of undergrads. A commendable achievement indeed. The day with the most downloads was Jul 21, 2024 with 141 downloads.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
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Dependencies
- Imports3 packages
- Linking To2 packages