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NPBayesImpute: Non-Parametric Bayesian Multiple Imputation for Categorical Data
These routines create multiple imputations of missing at random categorical data, with or without structural zeros. Imputations are based on Dirichlet process mixtures of multinomial distributions, which is a non-parametric Bayesian modeling approach that allows for flexible joint modeling.
Getting started
Browse package contents
Package details |
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| Author | Quanli Wang, Daniel Manrique-Vallier, Jerome P. Reiter and Jingchen Hu |
| Maintainer | Quanli Wang <quanli@stat.duke.edu> |
| License | GPL (>= 3) |
| Version | 0.6 |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
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