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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>.
| Version: | 0.5 |
| Depends: | Rcpp (≥ 0.10.2) |
| Imports: | methods, rlang, reshape2, ggplot2, dplyr, bayesplot |
| LinkingTo: | Rcpp |
| Published: | 2022-10-03 |
| Author: | Quanli Wang, Daniel Manrique-Vallier, Jerome P. Reiter and Jingchen Hu |
| Maintainer: | Jingchen Hu <jingchen.monika.hu at gmail.com> |
| License: | GPL (≥ 3) |
| NeedsCompilation: | yes |
| In views: | MissingData |
| CRAN checks: | NPBayesImputeCat results |
| Reference manual: | NPBayesImputeCat.pdf |
| Package source: | NPBayesImputeCat_0.5.tar.gz |
| Windows binaries: | r-devel: NPBayesImputeCat_0.5.zip, r-release: NPBayesImputeCat_0.5.zip, r-oldrel: NPBayesImputeCat_0.5.zip |
| macOS binaries: | r-release (arm64): NPBayesImputeCat_0.5.tgz, r-oldrel (arm64): NPBayesImputeCat_0.5.tgz, r-release (x86_64): NPBayesImputeCat_0.5.tgz, r-oldrel (x86_64): NPBayesImputeCat_0.5.tgz |
| Old sources: | NPBayesImputeCat archive |
| Reverse imports: | clusterMI |
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