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.


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Reference manual

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install.packages("NPBayesImputeCat")

0.1 by Jingchen Hu, 6 months ago


Browse source code at https://github.com/cran/NPBayesImputeCat


Authors: Quanli Wang , Daniel Manrique-Vallier , Jerome P. Reiter and Jingchen Hu


Documentation:   PDF Manual  


Task views: Missing Data


GPL (>= 3) license


Depends on methods, Rcpp

Linking to Rcpp


See at CRAN