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.
| Version: | 0.6 |
| Depends: | methods, Rcpp (≥ 0.10.2) |
| LinkingTo: | Rcpp |
| Published: | 2016-02-09 |
| Author: | Quanli Wang, Daniel Manrique-Vallier, Jerome P. Reiter and Jingchen Hu |
| Maintainer: | Quanli Wang <quanli at stat.duke.edu> |
| License: | GPL (≥ 3) |
| NeedsCompilation: | yes |
| CRAN checks: | NPBayesImpute results |
| Reference manual: | NPBayesImpute.pdf |
| Package source: | NPBayesImpute_0.6.tar.gz |
| Windows binaries: | r-devel: NPBayesImpute_0.6.zip, r-release: NPBayesImpute_0.6.zip, r-oldrel: NPBayesImpute_0.6.zip |
| OS X Mavericks binaries: | r-release: NPBayesImpute_0.6.tgz, r-oldrel: NPBayesImpute_0.6.tgz |
| Old sources: | NPBayesImpute archive |
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