NPBayesImpute: Non-Parametric Bayesian Multiple Imputation for Categorical Data

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

Downloads:

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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