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) .


Reference manual

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0.2 by Jingchen Hu, a year ago

Browse source code at

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