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>.

Version: 0.5
Depends: Rcpp (≥ 0.10.2)
Imports: methods, rlang, reshape2, ggplot2, dplyr, bayesplot
LinkingTo: Rcpp
Published: 2022-10-03
DOI: 10.32614/CRAN.package.NPBayesImputeCat
Author: Quanli Wang, Daniel Manrique-Vallier, Jerome P. Reiter and Jingchen Hu
Maintainer: Jingchen Hu < at>
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:, r-release:, r-oldrel:
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 dependencies:

Reverse imports: clusterMI


Please use the canonical form to link to this page.