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: 0.2
Depends: methods, Rcpp (≥ 0.10.2)
LinkingTo: Rcpp
Published: 2019-11-08
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
CRAN checks: NPBayesImputeCat results

Downloads:

Reference manual: NPBayesImputeCat.pdf
Package source: NPBayesImputeCat_0.2.tar.gz
Windows binaries: r-devel: NPBayesImputeCat_0.2.zip, r-devel-gcc8: NPBayesImputeCat_0.2.zip, r-release: NPBayesImputeCat_0.2.zip, r-oldrel: NPBayesImputeCat_0.2.zip
OS X binaries: r-release: NPBayesImputeCat_0.2.tgz, r-oldrel: NPBayesImputeCat_0.2.tgz
Old sources: NPBayesImputeCat archive

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