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>.

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Description file content

Package
NPBayesImputeCat
Type
Package
Title
Non-Parametric Bayesian Multiple Imputation for Categorical Data
Version
0.2
Date
2019-10-10
Author
Quanli Wang, Daniel Manrique-Vallier, Jerome P. Reiter and Jingchen Hu
Maintainer
Jingchen Hu
Description
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) .
License
GPL (>= 3)
Depends
methods, Rcpp (>= 0.10.2)
LinkingTo
Rcpp
RcppModules
clcm
NeedsCompilation
yes
Repository
CRAN
Packaged
2019-11-08 19:43:47 UTC; jingchenmonikahu
Date/Publication
2019-11-08 22:40:08 UTC

install.packages('NPBayesImputeCat')

0.2

7 days ago

Jingchen Hu

GPL (>= 3)

Depends on

methods, Rcpp (>= 0.10.2)

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