NPBayesImputeCat
Non-Parametric Bayesian Multiple Imputation for Categorical Data
These routines create multiple imputations of missing at random categorical data, and create multiply imputed synthesis of categorical data, with or without structural zeros. Imputations and syntheses are based on Dirichlet process mixtures of multinomial distributions, which is a non-parametric Bayesian modeling approach that allows for flexible joint modeling, described in Manrique-Vallier and Reiter (2014) doi:10.1080/10618600.2013.844700.
- Version0.5
- R versionunknown
- LicenseGPL (≥ 3)
- Needs compilation?Yes
- Last release10/03/2022
Documentation
Team
Jingchen Hu
Daniel Manrique-Vallier
Quanli Wang
Jerome P. Reiter
Insights
Last 30 days
This package has been downloaded 200 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 4 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 3,339 times in the last 365 days. Now we’re talking! This work is officially 'heard of in academic circles', just like those wild research papers on synthetic bananas. The day with the most downloads was Jul 21, 2024 with 75 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.
Data provided by CRAN
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Dependencies
- Depends1 package
- Imports5 packages
- Linking To1 package
- Reverse Imports1 package