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:	0.6
Depends:	Rcpp (≥ 0.10.2)
Imports:	methods, rlang, reshape2, ggplot2, dplyr, bayesplot, coda
LinkingTo:	Rcpp
Published:	2025-12-01
DOI:	10.32614/CRAN.package.NPBayesImputeCat
Author:	Quanli Wang [aut], Daniel Manrique-Vallier [aut], Jerome P. Reiter [aut], Jingchen Hu [aut, cre]
Maintainer:	Jingchen Hu <jingchen.monika.hu at gmail.com>
License:	GPL (≥ 3)
NeedsCompilation:	yes
In views:	MissingData
CRAN checks:	NPBayesImputeCat results [issues need fixing before 2026-03-19]

Documentation:

Reference manual:

NPBayesImputeCat.html , NPBayesImputeCat.pdf

Downloads:

Package source:	NPBayesImputeCat_0.6.tar.gz
Windows binaries:	r-devel: NPBayesImputeCat_0.6.zip, r-release: NPBayesImputeCat_0.6.zip, r-oldrel: NPBayesImputeCat_0.6.zip
macOS binaries:	r-release (arm64): NPBayesImputeCat_0.6.tgz, r-oldrel (arm64): NPBayesImputeCat_0.6.tgz, r-release (x86_64): NPBayesImputeCat_0.6.tgz, r-oldrel (x86_64): NPBayesImputeCat_0.6.tgz
Old sources:	NPBayesImputeCat archive

Reverse dependencies:

Reverse imports:

clusterMI

Linking:

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